Systems and methods for controlling electromagnetic heating of a hydrocarbon medium

ABSTRACT

Systems and methods for controlling heating of a hydrocarbon medium using a signal generator and a load having frequency and time dependent impedance. A desired heating life cycle is determined. A current state is determined using a model of the medium and the load. A desired operational state is determined from the current operational state and the desired heating life cycle. The desired operational state is selected to maximize a fit between the desired operational state and the desired heating life cycle. Desired signal generator control settings are determined for the signal generator in order to achieve the desired operational state. An output signal is generated using the signal generator by applying the at least one desired signal generator control setting to the signal generator. The output signal is defined to excite the load and thereby heat the hydrocarbon medium.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Patent ApplicationNo. 63/015,057 filed Apr. 24, 2020 and titled “SYSTEMS AND METHODS FORCONTROLLING ELECTROMAGNETIC HEATING OF A HYDROCARBON MEDIUM”, the entirecontents of which are hereby incorporated by reference for all purposes.

FIELD

The embodiments described herein relate to electromagnetic heating, andin particular to systems and methods for controlling electromagneticheating of a hydrocarbon medium.

BACKGROUND

The following is not an admission that anything discussed below is partof the prior art or part of the common general knowledge of a personskilled in the art.

Signal generators can be used to generate a variety of electricalsignals. Certain electrical signals generated by a signal generator canbe applied to a load to produce electromagnetic (EM) energy. Variousproperties of the electrical signals and the load may affect the EMenergy produced by the load. For example, the load may have afrequency-dependent impedance which attenuates the EM energy based onthe frequency of the electrical signals.

EM energy can be used to heat hydrocarbons. Similar to traditionalsteam-based technologies, the application of EM energy to heathydrocarbons can reduce viscosity and mobilize bitumen and heavy oil forproduction or transportation.

EM heating of hydrocarbon formations can be achieved by using a load,such as an EM radiator, antenna, applicator, or lossy transmission line,positioned inside an underground reservoir to radiate, or couple, EMenergy to the hydrocarbon formation. Hydrocarbon formations can includeheavy oil formations, oil sands, tar sands, carbonate formations, shaleoil formations, and any other hydrocarbon bearing formations, or anyother mineral. It may be desirable to control the EM energy produced bya load in order to more efficiently produce or transport hydrocarbons.

SUMMARY

This summary is intended to introduce the reader to the more detaileddescription that follows and not to limit or define any claimed or asyet unclaimed invention. One or more inventions may reside in anycombination or sub-combination of the elements or process stepsdisclosed in any part of this document including its claims and figures.

The various embodiments described herein generally relate to systems andmethods for controlling electromagnetic heating of a hydrocarbon medium.

In accordance with an aspect of this disclosure, there is provided amethod for controlling, using a processor, electromagnetic heating of ahydrocarbon medium using a signal generator and a load having afrequency dependent and time dependent and amplitude dependentimpedance. The method involves: determining a desired heating life cyclefor the hydrocarbon medium; determining a current operational stateusing a model of at least the hydrocarbon medium and the load;determining a desired operational state based on the current operationalstate and the desired heating life cycle, wherein the desiredoperational state is selected to maximize a fit between the desiredoperational state and the desired heating life cycle; determining atleast one desired signal generator control setting for the signalgenerator, wherein the at least one desired signal generator controlsetting is selected to provide the desired operational state; andgenerating an output signal using the signal generator by applying theat least one desired signal generator control setting to the signalgenerator, wherein the output signal is defined to excite the load andthereby heat the hydrocarbon medium.

In any embodiment, the desired heating life cycle may define a heatingprofile for the load, where the heating profile varies with time; thecurrent operational state may be determined for a present time; and thedesired operational state may be selected for a future time to maximizethe fit between the desired operational state and a desired state of thedesired heating life cycle at the future time.

In any embodiment, the desired operational state may be selected for afuture time to maximize the fit between the operational state as itevolves over time and the heating life cycle.

In any embodiment, the method may include: determining the currentoperational state for a present time; determining a difference betweenthe current operational state for the present time and the desiredheating life cycle for the present time; and updating the desiredheating life cycle using the difference.

In any embodiment, the load may include at least one radiating structurepositioned in the hydrocarbon medium. When the load is excited by theoutput signal, electromagnetic energy is coupled into the hydrocarbonmedium by the load.

In any embodiment, a standing electromagnetic wave may be produced alonga length of the at least one radiating structure through the coupling ofthe electromagnetic energy into the hydrocarbon medium.

In any embodiment, the at least one desired signal generator controlsetting may define a sequence of state transitions; applying the atleast one desired signal generator control setting to the signalgenerator may include adjusting the signal generator between a pluralityof signal generator states according to the sequence of statetransitions; and the sequence of state transitions may be defined toprovide a desired waveform for the output signal.

In any embodiment, the model may include at least one model parameter,and determining the current operational state may involve: determining astatus of the at least one model parameter; generating an updated modelby updating the model using the status of the at least one modelparameter; and determining the current operational state from theupdated model.

In any embodiment, each model parameter in the at least one modelparameter may include an expected status of one or more properties of atleast one of the signal generator, the load, and the hydrocarbon medium,where the one or more properties includes at least one of temperature,pressure, water concentration, current, voltage, impedance, andfrequency, and determining the status of the at least one modelparameter may involve: for a given model parameter in the at least onemodel parameter: determining an actual status of the one or moreproperties of at least one of the signal generator, the load, and thehydrocarbon medium corresponding to that given model parameter; andupdating the expected status to correspond to the actual status.

In any embodiment, determining the actual status of the one or moreproperties may involve: applying at least one sensing signal to theload; measuring at least one reflected sensing signal from the load; anddetermining the actual status of the one or more properties using the atleast one reflected sensing signal.

In any embodiment, determining the actual status of the one or moreproperties may involve: prior to applying the at least one sensingsignal to the load, applying an output signal from the signal generatorto the load.

In any embodiment, determining the actual status of the one or moreproperties may involve: prior to applying the at least one sensingsignal to the load, disabling an output signal from the signal generatorto the load.

In any embodiment, the at least one sensing signal may include at leasttwo sensing signals, each of the at least two sensing signals beingorthogonal with respect to the other sensing signals.

In any embodiment, the status of the at least one model parameter may bedetermined based on at least one of historical data and a machinelearning model.

In any embodiment, the model may include at least one of anelectromagnetic property, a thermal property, a fluid property, and astructural property.

In any embodiment, the model may include a transverse electromagneticmode that may form a standing wave along a length of the load.

In any embodiment, the at least one constraint for the signal generatormay include at least one of a voltage range, a current range, afrequency range, a temperature range, a maximum completion time, and aminimum power.

In any embodiment, the desired operational state may include at leastone of a spatial heating profile along a length of the load, a powerspectral density of the output signal, and a standing electromagneticwave pattern along a length of the load.

In any embodiment, determining the desired operational state mayinclude: determining a plurality of potential operational states basedon the model; determining a plurality of potential cost penalties by,for each potential operational state in the plurality of potentialoperational states determining a potential cost penalty associated thatpotential operational state using the desired heating life cycle;determining a minimum cost operational state of the plurality ofpotential operational states, the minimum cost operational stateassociated with a lowest cost penalty of the plurality of costpenalties; and identifying the minimum cost operational state as thedesired operational state.

In any embodiment, the desired operational state may include at leastone arcing condition.

In any embodiment, a predicted future operational state may include apredicted arcing condition. Such an operational state may thereby beavoided to mitigate the possibility of arcing.

In any embodiment, determining the at least one desired signal generatorcontrol setting may be further based on at least one of historical dataand a machine learning model.

In any embodiment, the method may further include: determining at leastone desired load control setting for the load based on the desiredoperational state; and applying the at least one desired load controlsetting to the load.

In any embodiment, the method may further include: determining at leastone desired solvent control setting for a solvent control unit based onthe desired operational state, the solvent control unit for providingsolvent to the hydrocarbon medium; and applying the at least one desiredsolvent control setting to the solvent control unit.

In accordance with an aspect of this disclosure, there is provided asystem for controlling electromagnetic heating a hydrocarbon mediumusing a signal generator and a load having a frequency dependent andtime dependent and amplitude dependent impedance. The system includes aprocessor configured to: determine a desired heating life cycle for thehydrocarbon medium; determine a current operational state, using a modelof at least the hydrocarbon medium and the load; determine a desiredoperational state based on the current operational state and the desiredheating life cycle, wherein the desired operational state is selected tomaximize a fit between the desired operational state and the desiredheating life cycle; determine at least one desired signal generatorcontrol setting for the signal generator, wherein the at least onedesired signal generator control setting is selected to provide thedesired operational state; and apply the at least one desired signalgenerator control setting to the signal generator, wherein the signalgenerator generates an output signal in response to the applied at leastone desired signal generator control setting, and wherein the outputsignal is defined to excite the load and thereby heat the hydrocarbonmedium.

In any embodiment, the processor may be configured to determine thedesired heating life cycle to include a heating profile for the load,where the heating profile varies with time; determine the currentoperational state for a present time; and select the desired operationalstate for a future time to maximize the fit between the desiredoperational state and a desired state of the desired heating life cycleat the future time.

In any embodiment, the processor may be configured to determine thecurrent operational state for a present time; determine a differencebetween the current operational state for the present time and thedesired heating life cycle for the present time; and update the desiredheating life cycle using the difference.

In any embodiment, the load may include at least one radiating structurepositioned in the hydrocarbon medium. When the load is excited by theoutput signal, electromagnetic energy is coupled into the hydrocarbonmedium by the load.

In any embodiment, a standing electromagnetic wave may be produced alonga length of the at least one radiating structure through the coupling ofthe electromagnetic energy into the hydrocarbon medium.

In any embodiment, the at least one desired signal generator controlsetting may define a sequence of state transitions; the processor may beconfigured to apply the at least one desired signal generator controlsetting to the signal generator by adjusting the signal generatorbetween a plurality of signal generator states according to the sequenceof state transitions; and the sequence of state transitions may bedefined to provide a desired waveform for the output signal.

In any embodiment, the model may include at least one model parameterand the processor may be configured to determine the current operationalstate by: determining a status of the at least one model parameter;generating an updated model by updating the model using the status ofthe at least one model parameter; and determining the currentoperational state from the updated model.

In any embodiment, each model parameter in the at least one modelparameter may include an expected status of one or more properties of atleast one of the signal generator, the load, and the hydrocarbon medium,wherein the one or more properties comprises at least one oftemperature, pressure, water concentration, current, voltage, impedance,and frequency; and the system may further include: at least one sensoroperable to measure an actual status of the one or more properties of atleast one of the signal generator, the load, and the hydrocarbon medium.Determining the status of the at least one model parameter may include:for a given model parameter in the at least one model parameter:determining the actual status of the one or more properties of at leastone of the signal generator, the load, and the hydrocarbon mediumcorresponding to that given model parameter; and updating the expectedstatus to correspond to the actual status.

In any embodiment, determining the actual status of the one or moreproperties may include: applying at least one sensing signal to theload; measuring at least one reflected sensing signal from the load; anddetermining the actual status of the one or more properties using the atleast one reflected sensing signal.

In any embodiment, determining the actual status of the one or moreproperties may include: prior to applying at least one sensing signal tothe load, applying an output signal from the signal generator to theload.

In any embodiment, determining the actual status of the one or moreproperties may include: prior to applying at least one sensing signal tothe load, disabling an output signal from the signal generator to theload.

In any embodiment, the at least one sensing signal may include at leasttwo sensing signals, each of the at least two sensing signals beingorthogonal with respect to the other sensing signals.

In any embodiment, the processor may be configured to determine thestatus of the at least one model parameter based on at least one ofhistorical data and a machine learning model.

In any embodiment, the model may include at least one of anelectromagnetic property, a thermal property, a fluid property, and astructural property.

In any embodiment, the model may include a transverse electromagneticmode forming a standing wave along a length of the load.

In any embodiment, the at least one constraint for the signal generatormay include at least one of a voltage range, a current range, afrequency range, a temperature range, a maximum completion time, and aminimum power.

In any embodiment, the desired operational state may include at leastone of a spatial heating profile along a length of the load, a powerspectral density of the output signal, and a standing electromagneticwave pattern along a length of the load.

In any embodiment, the processor may be configured to determine thedesired operational state by: determining a plurality of potentialoperational states based on the model; determining a plurality ofpotential cost penalties by, for each potential operational state in theplurality of potential operational states determining a potential costpenalty associated that potential operational state using the desiredheating life cycle; determining a minimum cost operational state of theplurality of potential operational states, the minimum cost operationalstate associated with a lowest cost penalty of the plurality of costpenalties; and identifying the minimum cost operational state as thedesired operational state.

In any embodiment, the desired operational state may include at leastone arcing condition.

In any embodiment, the processor may be configured to determine the atleast one desired signal generator control setting is further based onat least one of historical data and a machine learning model.

In any embodiment, the processor may be further configured to: determineat least one desired load control setting for the load based on thedesired operational state; and apply the at least one desired loadcontrol setting to the load.

In any embodiment, the processor may be further configured to: determineat least one desired solvent control setting for a solvent control unitbased on the desired operational state, the solvent control unit forproviding solvent to the hydrocarbon medium; and apply the at least onedesired solvent control setting to the solvent control unit.

In accordance with an aspect of this disclosure, there is provided asystem for electromagnetic heating a hydrocarbon medium. The systemincludes a signal generator, a load, and a processor. The signalgenerator can generate an output signal. The load has a frequencydependent and time dependent impedance and can be excited by the outputsignal to heat the hydrocarbon medium. The processor is configured to:determine a desired heating life cycle for the hydrocarbon medium; acurrent operational state, using a model of at least the hydrocarbonmedium and the load; determine a desired operational state based on thecurrent operational state and the desired heating life cycle, whereinthe desired operational state is selected to maximize a fit between thedesired operational state and the desired heating life cycle; determineat least one desired signal generator control setting for the signalgenerator, wherein the at least one desired signal generator controlsetting is selected to provide the desired operational state; and applythe at least one desired signal generator control setting to the signalgenerator, wherein the signal generator generates an output signal inresponse to the applied at least one desired signal generator controlsetting, and wherein the output signal is defined to excite the load andthereby heat the hydrocarbon medium.

It will be appreciated that the aspects and embodiments may be used inany combination or sub-combination. Further aspects and advantages ofthe embodiments described herein will appear from the followingdescription taken together with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the embodiments described herein and toshow more clearly how they may be carried into effect, reference willnow be made, by way of example only, to the accompanying drawings whichshow at least one exemplary embodiment, and in which:

FIG. 1 is profile view of an example system for electromagnetic heatingof a hydrocarbon formation in accordance with an embodiment;

FIG. 2A is a block diagram of an example system for controllingelectromagnetic heating of a hydrocarbon medium in accordance with anembodiment;

FIG. 2B is a block diagram of an example signal generator that may beused with the system of FIG. 2A in accordance with an embodiment;

FIG. 2C is a block diagram of an example heating life cycle controllersub-unit that may be used with the system of FIG. 2A in accordance withan embodiment;

FIG. 2D is a block diagram of an example model parameter generator thatmay be used with the system of FIG. 2A in accordance with an embodiment;

FIG. 2E is a block diagram of an example control setting generator thatmay be used with the system of FIG. 2A in accordance with an embodiment;

FIG. 3A is an illustration of example electromagnetic waves that may begenerated using the system of FIG. 1 in accordance with an embodiment;

FIG. 3B is a schematic illustration of an example radiating structuremodel that may be used with the system of FIG. 1 in accordance with anembodiment;

FIG. 4 is a flow chart of an example method for controllingelectromagnetic heating of a hydrocarbon medium in accordance with anembodiment;

FIGS. 5A and 5B are illustrations of example sensing signal measurementsin accordance with an embodiment;

FIGS. 6A and 6B are illustrations of example sensing signal measurementsin accordance with an embodiment;

FIG. 7 is a graph illustrating an example output signal in accordancewith an embodiment;

FIGS. 8A and 8B are illustrations of example models for anelectromagnetic heating apparatus in accordance with an embodiment; and

FIGS. 9A and 9B are schematic diagrams of example equivalent circuitsfor an electromagnetic heating apparatus in accordance with anembodiment.

The skilled person in the art will understand that the drawings,described below, are for illustration purposes only. The drawings arenot intended to limit the scope of the applicants' teachings in any way.Also, it will be appreciated that for simplicity and clarity ofillustration, elements shown in the figures have not necessarily beendrawn to scale. For example, the dimensions of some of the elements maybe exaggerated relative to other elements for clarity. Further, whereconsidered appropriate, reference numerals may be repeated among thefigures to indicate corresponding or analogous elements.

DESCRIPTION OF VARIOUS EMBODIMENTS

It will be appreciated that numerous specific details are set forth inorder to provide a thorough understanding of the exemplary embodimentsdescribed herein. However, it will be understood by those of ordinaryskill in the art that the embodiments described herein may be practicedwithout these specific details. In other instances, well-known methods,procedures and components have not been described in detail so as not toobscure the embodiments described herein. Furthermore, this descriptionis not to be considered as limiting the scope of the embodimentsdescribed herein in any way, but rather as merely describing theimplementation of the various embodiments described herein.

It should be noted that terms of degree such as “substantially”, “about”and “approximately” when used herein mean a reasonable amount ofdeviation of the modified term such that the end result is notsignificantly changed. These terms of degree should be construed asincluding a deviation of the modified term if this deviation would notnegate the meaning of the term it modifies.

In addition, as used herein, the wording “and/or” is intended torepresent an inclusive-or. That is, “X and/or Y” is intended to mean Xor Y or both, for example. As a further example, “X, Y, and/or Z” isintended to mean X or Y or Z or any combination thereof.

The terms “including,” “comprising” and variations thereof mean“including but not limited to,” unless expressly specified otherwise. Alisting of items does not imply that any or all of the items aremutually exclusive, unless expressly specified otherwise. The terms “a,”“an” and “the” mean “one or more,” unless expressly specified otherwise.

As used herein and in the claims, two or more elements are said to be“coupled”, “connected”, “attached”, or “fastened” where the parts arejoined or operate together either directly or indirectly (i.e., throughone or more intermediate parts), so long as a link occurs. As usedherein and in the claims, two or more elements are said to be “directlycoupled”, “directly connected”, “directly attached”, or “directlyfastened” where the element are connected in physical contact with eachother. None of the terms “coupled”, “connected”, “attached”, and“fastened” distinguish the manner in which two or more elements arejoined together.

The terms “an embodiment,” “embodiment,” “embodiments,” “theembodiment,” “the embodiments,” “one or more embodiments,” “someembodiments,” and “one embodiment” mean “one or more (but not all)embodiments of the present invention(s),” unless expressly specifiedotherwise.

Embodiments described herein may relate to and/or involve the use oftime-harmonic signals. As a skilled reader will appreciate, referencesto phase shifts or phase differences between time-harmonic (e.g. asingle frequency sinusoidal) signals can also be expressed as a timedelay. For time harmonic signals, time delay and phase difference conveythe same physical effect. For example, a 180° phase difference betweentwo time-harmonic signals of the same frequency can also be referred toas a half-period delay. As a further example, a 90° phase difference canalso be referred to as a quarter-period delay. References to timedelay(s) may be used as a more general term for comparing periodicsignals. For instance, if the periodic signals contain multiplefrequencies (e.g. a series of rectangular or triangular pulses), thenthe time lag between two such signals having the same fundamentalharmonic may be referred to as a time delay. For simplicity, in thedescription that follows, in the case of single frequency sinusoidalsignals the term “phase shift” shall be used. In the case ofmulti-frequency periodic signals, the term “phase shift” will beunderstood to refer to the time delay equal to the corresponding timedelay of the fundamental harmonic of the two signals.

As used herein, the term “radio frequency” may extend beyond theconventional meaning of radio frequency. As used herein, the term “radiofrequency” generally includes frequencies at which the physicaldimensions of system components are comparable to the wavelength of theEM wave. System components that are between approximately 1/16 of awavelength to 10 wavelengths can be considered comparable to thewavelength. For example, a 1 kilometer (km) long underground system thatuses EM energy to heat underground formations and operates at 50kilohertz (kHz) will have physical dimensions that are comparable to thewavelength. If the underground formation has significant water content,(e.g., relative electrical permittivity being approximately 60 andconductivity being approximately 0.002 S/m), the EM wavelength at 50 kHzis 303 meters. The length of the 1 km long radiator is approximately 3.3wavelengths. If the underground formation is dry (e.g., relativeelectrical permittivity being approximately 6 and conductivity beingapproximately 3E-7 S/m), the EM wavelength at 50 kHz is 2450 meters. Thelength of the radiator is then approximately 0.4 wavelengths. Therefore,in both wet and dry scenarios, the length of the radiator is consideredcomparable to the wavelength in the context of the disclosure herein.Accordingly, effects typically seen in conventional radio-frequency (RF)systems will be present and while a frequency of 50 kHz is not typicallyconsidered an RF frequency, in the disclosure herein such a system maybe considered to be an RF system.

Referring to FIG. 1 , shown therein is a profile view of an apparatus100 for electromagnetic heating of hydrocarbon formations in accordancewith an embodiment. The apparatus 100 can be used for electromagneticheating of a hydrocarbon formation 102. The apparatus 100 includes anelectrical power source 106, an electromagnetic (EM) wave generator 108(also referred to as a signal generator), a waveguide portion 110, andtransmission line conductor portion 112. As will be appreciated, theapparatus 100 shown in FIG. 1 is provided for illustration purposes onlyand other suitable configurations of an apparatus for electromagneticheating of hydrocarbon formations are possible.

As shown in FIG. 1 , the electrical power source 106 and theelectromagnetic wave generator 108 can be located at the surface 104.Alternately, one or both of the electrical power source 106 and theelectromagnetic wave generator 108 can be located below ground.

The electrical power source 106 generates electrical power. Theelectrical power source 106 can be any appropriate source of electricalpower, such as a stand-alone electric generator or an electrical grid.The electrical power source 106 may include transformers and/orrectifiers for providing electrical power with desired and/or requiredparameters. The electrical power may be one of alternating current (AC)or direct current (DC). Power cables 114 carry the electrical power fromthe electrical power source 106 to the EM wave generator 108.

The EM wave generator 108 generates EM power. It will be understood thatEM power can be generated in various forms including high frequencyalternating current, alternating voltage, current waves, or voltagewaves. For example, the EM power can be a periodic high frequency signalhaving a fundamental frequency (f0). The high frequency signal may havea sinusoidal waveform, square waveform, or any other appropriate signalshape. The high frequency signal can further include harmonics of thefundamental frequency. For example, the high frequency signal caninclude second harmonic 2f₀, and third harmonic 3f₀ of the fundamentalfrequency f₀. In some embodiments, the EM wave generator 108 can producemore than one frequency at a time. In some embodiments, the frequencyand shape of the high frequency signal may change over time. The term“high frequency alternating current”, as used herein, broadly refers toa periodic, high frequency EM power signal, which in some embodiments,can be a voltage signal.

As noted above, the EM wave generator 108 may be located above-ground.An apparatus with the EM wave generator 108 located above ground ratherthan underground can be easier to deploy.

Alternately, the EM wave generator may be located underground. When theEM wave generator 108 is located underground, transmission losses may bereduced because EM energy is not dissipated in areas that do not producehydrocarbons (e.g. along the waveguide portion distance between the EMwave generator 108 and the transmission line conductor portion 112).

The waveguide portion 110 can carry high frequency alternating currentfrom the EM wave generator 108 to the transmission line conductors 112 aand 112 b. Each of the transmission line conductors 112 a and 112 b canbe coupled to the EM wave generator 108 via individual waveguides 110 aand 110 b. As shown in FIG. 1 , the waveguides 110 a and 110 b can becollectively referred to as the waveguide portion 110.

Each of the waveguides 110 a and 110 b can extend between a respectiveproximal end and a distal end. The proximal ends of each waveguide 110 aand 110 b can be connected to the EM wave generator 108. The distal endsof each waveguide 110 a and 110 b can be connected to the transmissionline conductors 112 a and 112 b respectively.

As shown in the example of FIG. 1 , each waveguide 110 a and 110 b canbe provided by a coaxial transmission line having an outer conductor 118a and 118 b and an inner conductor 120 a and 120 b, respectively. Forexample, each of the waveguides 110 a and 110 b may be provided using ametal casing pipe as the outer conductor with the metal casingsconcentrically surrounding pipes, cables, wires, or conductor rods, asthe inner conductors. Optionally, the outer conductors 118 a and 118 bcan be positioned within at least one additional casing pipe along atleast part of the length of the waveguide portion 110.

The transmission line conductor portion 112 can be coupled to the EMwave generator 108 via the waveguide portion 110. As shown in FIG. 1 ,the transmission line conductors 112 a and 112 b may be collectivelyreferred to as the transmission line portion 112. In the example shownin FIG. 1 , the transmission line portion 112 includes two transmissionline conductors 112 a and 112 b. Optionally, the transmission lineportion 112 may also include additional transmission line conductors.

Various configurations of the transmission line conductors 112 a and 112b may be used. For example, both transmission line conductors 112 a and112 b may be defined by a pipe. Alternately, only one or none of thetransmission line conductors 112 a and 112 b may be defined by a pipe.

Alternately or in addition, one or both of the transmission lineconductors 112 a and 112 b may be provided using conductor rods, coiledtubing, or coaxial cables, or any other suitable conduit usable topropagate EM energy from EM wave generator 108.

In the example shown in FIG. 1 , the transmission line conductors 112 aand 112 b are positioned in direct contact with the hydrocarbonformation 102. Alternately, the transmission line conductors 112 may beelectrically isolated or partially electrically isolated from thehydrocarbon formation 102.

The transmission line conductors 112 a and 112 b have a proximal end(proximate the waveguide portion 110) and a distal end (spaced apartfrom the waveguide portion 110). The proximal end of each transmissionline conductor 112 a and 112 b can be coupled to the EM wave generator108. For example, the proximal end of each transmission line conductor112 a and 112 b can be coupled to the EM wave generator 108 via thecorresponding waveguides 110 a and 110 b as shown in FIG. 1 .

The transmission line conductors 112 a and 112 b can be excited by thehigh frequency alternating current generated by the EM wave generator108. When excited, the transmission line conductors 112 a and 112 b canform an open transmission line that includes transmission lineconductors 112 a and 112 b and medium 102. The transmission line canpropagate EM energy that is contained within a cross-section of a radiusof several meters to several tens of meters depending on the frequencyof excitation. The open transmission line can propagate an EM wave fromthe proximal end of the transmission line conductors 112 a and 112 b tothe distal end of the transmission line conductors 112 a and 112 b. Theopen transmission line can also propagate a reflected EM wave in theopposite direction from the distal end to the proximal end uponreflection of the EM wave at the distal end.

Optionally, the EM wave may establish a standing wave along thetransmission line 112. Alternately, the propagating electromagnetic wavemay form a standing electromagnetic wave or an exponentially decayingwave depending on the loss properties of the medium and the frequency ofgenerator excitation.

An open transmission line can carry and dissipate energy within thedielectric medium. In the example of apparatus 100, the hydrocarbonformation 102 between the transmission line conductors 112 a and 112 bcan act as a dielectric medium for the open transmission line formed bythe transmission line conductors 112 a and 112 b. The open transmissionline can carry and dissipate energy within this dielectric medium, thatis, the hydrocarbon formation 102.

The open transmission line carrying EM energy within the hydrocarbonformation 102 may be referred to as a “dynamic transmission line” asmedium properties change over time. The transmission line conductors 112can be configured to propagate an EM wave in both directions asdescribed above. This can allow the dynamic transmission line to carryEM energy within long well bores (as used herein, well bores spanning alength of 500 meters (m) to 1500 meters (m) or more can be consideredlong well bored).

Producer well 122 is typically located at or near the bottom of theunderground reservoir. The producer well 122 can be configured toreceive heated oil released from the hydrocarbon formation 102 by the EMheating process. The heated oil can drain mainly by gravity to theproducer well 122.

The producer well 122 can define a longitudinal well axis. Thetransmission line conductors 112 a and 112 b may also extend alongrespective transmission line longitudinal axes. The longitudinal wellaxis and the transmission line longitudinal axes may be parallel or evencoincident. Thus, the transmission line conductors 112 a and 112 b mayextend in a direction generally parallel to the producer well 122 (e.g.along an axes coincident with a vertical projection of the producer well122).

As shown in the example of FIG. 1 , producer well 122 is substantiallyhorizontal (i.e., parallel to the surface). The transmission lineconductors 112 a and 112 b may also extend in a substantially horizontaldirection.

The producer well 122 may be located at the same depth or at a greaterdepth than (i.e. below) at least one of the transmission line conductors112 a, 112 b of the open transmission line 112. Alternately, theproducer well 122 can be located above the transmission line conductors112 a, 112 b of the open transmission line 112.

The producer well 122 may be positioned laterally in between thetransmission line conductors 112 a, 112 b. For example, the producerwell 122 may be positioned centered between the transmission lineconductors 112 a, 112 b. Alternately, the producer well 122 may bepositioned with any appropriate offset from the lateral center betweenthe transmission line conductors 112 a, 112 b. In some applications, itmay be advantageous to position the producer well 122 closer to a firsttransmission line conductors than a second transmission line conductor.This may allow the region closer to the first transmission lineconductor to be heated faster and contribute to early onset of oilproduction.

As the hydrocarbon formation 102 is heated, steam may also be releasedthat displaces the heated oil that has drained to and is collected inthe producer well 122. Steam may assist in driving heated oil toward theproducer in addition to gravity. The steam can accumulate in a steamchamber above the producer well 122. Direct contact between the steamchamber and the producer well 122 can result in a drop in systempressure, which can increase steam and water production but may reduceoil production. Thus, maintaining separation between the steam chamberand the producer well 122 for as long as possible during operation mayfacilitate increased oil production.

The open transmission line provided by the transmission line conductors112 may facilitate providing wide and flat heated areas. The width ofthe heated area can be varied by adjusting the lateral separationbetween the transmission line conductors 112 a and 112 b. However, thehydrocarbon formation 102 between the transmission line conductors 112 aand 112 b may not be heated uniformly until the whole hydrocarbonformation 102 between the transmission line conductors 112 a and 112 bis desiccated. Regions closer to the respective transmission lineconductors 112 a and 112 b may initially be heated much more stronglythan the regions further from the transmission line conductors 112 a and112 b, including the region between the transmission line conductors 112a and 112 b.

In some applications, it can be advantageous for the distance betweenthe transmission line conductors 112 a and 112 b to be narrow toencourage early onset of oil production. However, a wider distance (e.g.larger than 8 meters) between the transmission line conductors 112 a and112 b may encourage long term oil production by maintaining a separationbetween the producer well 122 and the steam chamber (i.e., maintaining adisconnected steam chamber).

Underground reservoir simulations indicate that heating an areaapproximately 2 meters to 8 meters above the producer well 122 cancreate a steam chamber that is more favorable than when the heated areais narrow, even if the total EM power used for heating is the same. Inthis context, a region of approximately 8 meters to 40 meters can beconsidered wide while a region with a width of less than approximately 8meters can be considered narrow. A more favorable steam chamber is achamber which stays ‘disconnected’ (i.e., remains separated) from theproducer well 122 for a longer period of time.

It may also be desirable to maximize the efficiency of the reservoirheating, to promote the cost effectiveness of oil production. Byfocusing the reservoir heating on oil producing regions of thehydrocarbon formations, rather than regions of poor oil saturation orwith physical barriers (e.g. shale) preventing oil flow, radiationlosses may be reduced and thus the overall production costs (both interms of monetary value and energy costs) may be reduced.

Producing heat laterally far from the open transmission line, whileminimizing heating of the under-burden (i.e., region below theunderground reservoir) and/or over-burden layers (i.e., region above theunderground reservoir) may promote efficiency in the oil productionprocess. Heating of the under-burden region and/or over-burden regiondoes not generally result in oil production, and therefore the energyused to heat these regions effectively represents radiation losses.

The EM wave generator 108 may be configured to accommodate a wide loadimpedance range. The electromagnetic properties of the hydrocarbonformation 102 may vary significantly throughout the heating process, andthus the EM wave generator 108 may be operable to respond to changes inthe hydrocarbon formation 102.

System 100 may be configured to operate according to a specifiedoperational life-cycle. The operational life-cycle can define a desiredheating life cycle for formation 102. The desired heating life cycle mayspecify the heating profile within the formation 102 over the course ofthe operational lifespan of the electromagnetic heating provided bysystem 100. The desired heating life-cycle (and correspondingoperational life-cycle) may be defined based on characteristics ofsystem 100, hydrocarbon medium 102, and the interaction between variouscomponents of system 100 and medium 102.

The heating life cycle is a component of the overall productionlife-cycle. The heating life cycle may be optimized accounting forvarious operational factors such as cost, yield, minimized energy usage,energy efficiency and so forth. The operational life-cycle can bedefined to optimize the efficiency of heating the medium 102 tofacilitate hydrocarbon extraction, subject to constraints imposed by thesystem 100 and the nature of the medium 102. In some cases, theoperational life-cycle (and the desired heating life cycle) may beadaptable or modifiable in response to feedback from various componentsof system 100, such as the generator 108 and/or sensors. System 100 canbe configured to monitor feedback from system component such as thegenerator 108 and sensors (not shown) to adapt the desired heating lifecycle to reflect the current state of the system 100, hydrocarbonformation 102 and the overall extraction process. System 102 can also beconfigured to update the desired heating life cycle based on predictivemodelling of the system 100, hydrocarbon formation 102 and the overallextraction process.

The desired heating life-cycle of system 100 can be defined to optimizeheating over various different heating phases predicted for the medium102. The desired heating life-cycle of system 100 may be defined inorder to provide desired heating characteristics (e.g. a desired spatialheating profile) along a corridor of the medium 102 over time. Thecorridor may be defined as the portion of the hydrocarbon medium 102surrounding the transmission line conductor portion 112. The desiredheating characteristics can be used to determine control settings forthe generator 108 by identifying the control settings expected toprovide the desired heating characteristics (or near to the desiredheating characteristics).

In some examples, heating of hydrocarbon formation 102 can may describedby four distinct heating phases, in which different electromagnetic,thermodynamic, and fluid-dynamic mechanisms may be present. Depending onthe length of the transmission line conductors 112, the variousproperties of the hydrocarbon formation 102, and the desired heatingstrategy, it may be desirable to operate the apparatus 100 to transitionbetween these different heating phases at different times.

The desired heating characteristics defined by the desired heatinglife-cycle may be specified to change over time as the characteristicsof the medium 102 change. The desired heating life-cycle may also beadjustable to adapt the desired heating characteristics in response tofeedback from various components of system 100, such as generator 108and/or one or more sensors, and/or outputs from predictive modelling ofthe system 100 and/or medium 102.

In a first heating phase, a high concentration of water may be presentin the regions of the hydrocarbon formation 102 surrounding thetransmission line conductors 112. As a result, impedance experienced byEM waves propagating along the transmission line conductors 112 will bemostly resistive, and high frequencies of the EM waves will be greatlyattenuated.

In a second heating phase, water begins to diffuse away or partiallyevaporate from areas near the transmission line conductors 112. Thewater reduction can decrease the conductivity of the hydrocarbonformation 102. At the same time, the temperature of the hydrocarbonformation 102 around the transmission line conductors 112 increases.This increase in temperature can increase the conductivity of thehydrocarbon formation 102, counteracting some or all of the decreasecaused by the water reduction.

In a third heating phase, water around the transmission line conductors112 vaporizes and carries heat away from the transmission lineconductors 112. The vaporized water can then condense and partiallydiffuse back toward the transmission line conductors 112, due to a waterconcentration gradient.

In a fourth heating phase, hydrocarbons begin to flow into the producerwell 122, reducing the pressure in the regions of the hydrocarbon medium102 near the transmission line conductors 112. More steam is produced inthis region, lowering the water concentration, and increasing theresistance. A steam chamber may be established during this heatingphase.

The desired heating characteristics defined by the desired heatinglife-cycle may change to reflect the different heating phases of theformation 102.

Referring to FIG. 2A, shown therein is a block diagram of an examplesystem 200 for controlling electromagnetic heating of a hydrocarbonmedium 209. The example electromagnetic heating control system 200includes a signal generator 206, a load 208, a controller 202, sensors210, and data sources 212.

In the example shown in FIG. 2A, only some of the components of theelectromagnetic heating control system 200 are depicted as beingpositioned within the hydrocarbon medium 209 in FIG. 2A. However, itwill be appreciated that any or all of the components of system 200 maybe positioned within hydrocarbon medium 209 in the embodiments describedherein.

The hydrocarbon medium 209 may refer to any formation, body, orstructure that stores or contains hydrocarbons. The hydrocarbon medium209 may be an underground formation. Alternately or in addition, thehydrocarbon medium 209 may include above ground storage.

In some embodiments, the electromagnetic heating control system 200 canbe implemented as the electromagnetic hydrocarbon heating apparatus 100shown in FIG. 1 . For example, the signal generator 206 can perform thefunctions of the electromagnetic wave generator 108, the load 208 may bedefined to include the transmission line conductors 112 and the couplingmember 207 provided by the waveguide portion 110.

The signal generator 206 is operable to generate one or more outputsignals that can be applied to load 208. The output signals generated bythe signal generator 206 can include more than one frequency. In someexamples, the output signals may include a band of frequencies.

The output signals can be generated with various different frequencies.For example, the output signals may be generated with a bandwidthbetween 0 to 1 kilohertz (kHz). Alternately or in addition, the outputsignals may be generated with a bandwidth between about 1 kilohertz(kHz) to about 100 megahertz (MHz). Alternately or in addition, theoutput signals may be generated with a bandwidth that is within theradio frequency (RF) band. An output signal generated by the signalgenerator 206 may be characterized by a power spectral density, or ameasure of the power of the signal as a function of frequency.

The signal generator 206 can include various components (not shown) thatcan be configured to vary the characteristics of the output signalsproduced. For example, the signal generator 206 may include one or morecomponents which can be configured to modify the frequency, voltage,current, power, phase, or other property of the output signals. Thesignal generator 206 may be configured to control the power spectraldensity of the output signal. The signal generator 206 may also includecomponents operable to vary the output impedance (or resistance orreactance) of the signal generator 206. There may be more than oneconfiguration of the signal generator 206 operable to result in the sameoutput signal and/or output impedance.

Optionally, signal generator 206 can be configured to generate an outputsignal that includes a plurality of pulses. For example, the signalgenerator 206 may include a switch module that includes a switchedH-bridge. The signal generator 206 may be configured to switch theH-bridge according to a specified pulse sequence of state transitions.The specified pulse sequence may be defined in order to provide desiredoperational characteristics for the output signal such as a desiredpower spectral density.

The signal generator 206 may include one or more signal generatingsub-units. Optionally, the signal generator 206 may also include signalconditioning components usable to adjust the characteristics of theoutput signal.

Referring now to FIG. 2B, shown therein is an example configuration ofthe signal generator 206 that may be used with the system 200 shown inFIG. 2A. As shown in the example of FIG. 2B, the signal generator 206may include a plurality of signal generation sub-units 220A-220N.Alternately, the signal generator 206 may include only a single signalgeneration sub-unit 220.

Each signal generation sub-unit 220 may be configured to generate anoutput signal portion. The output signal portion generated by each ofthe generator sub-units 220A-220N can be coupled to signal combiner 222.Signal combiner 222 can combine the one or more output signal portionsreceived from the generator sub-units 220A-220N to generate a combinedoutput signal. The combined output signal can then be provided to theload 208.

The signal combiner 222 can be implemented in various different ways.For example, the signal combiner 222 can include one or moretransformers. In some cases, the signal combiner 222 may includemultiple transformers e.g., each with a separate transformer core. Eachtransformer may be coupled to a corresponding one of the generatorsub-units 220.

Alternately, the signal combiner 222 may include only a singletransformer. For example, the signal combiner 222 may include a singletransformer with multiple primary windings and a single secondarywinding, with each winding sharing a common transformer core. Eachprimary winding can be coupled to a corresponding one of the generatorsub-units 220.

In some cases, the signal combiner 222 may include an arrangement ofother components, such as capacitors, inductors, or other components inaddition to, or in place of, the transformer(s). In some cases, thesignal combiner 222 may be a Wilkinson-type combiner.

Each generator sub-unit 220 can be configured to generate acorresponding output signal portion. Each generator sub-unit 220 may beadjustable between a plurality of sub-unit states. The individualgenerator sub-units 220 can be configured to adjust between the sub-unitstates in order to generate a desired signal output portion. Forexample, each generator sub-unit 220 may include a switch module that isadjustable between a plurality of switch states. The plurality ofsub-unit states for a given generator sub-unit 220 may be defined by theplurality of switch states for that generator sub-unit 220.

Controller 202 may be configured to define a sequence of statetransitions for the signal generator 206. The sequence of statetransitions can be defined in order to provide a desired waveform forthe output signal. The sequence of state transitions can be provided toone or more of the generator sub-units 220 to control adjustment of theindividual generator sub-unit 220 between the plurality of sub-unitsstates. In some cases, one or more generator sub-units 202 may beoperable in a static mode (i.e. in which the generator sub-unit 202remains in a fixed sub-unit state and does not transition betweensub-unit states) while one or more active generator sub-units 220 isadjusted according to a specified sequence of state transitions definedby controller 202. Controller 202 may be configured to active aspecified number and/or group of generator sub-units 220 to provide theoutput signal with desired signal characteristics.

In some examples, each generator sub-unit 220 may include a switchmodule. The switch module can include one or more switches. The switchmodule may be configured to can receive a module input signal andprovide a corresponding module output signal. The module output signalcan be used to generate the output signal portion for that generatorsub-unit 220.

The switch module can be provided in various arrangements. For example,the switch module may include a plurality of switches in an H-bridgeand/or half H-bridge arrangement. Alternately or in addition, the switchmodule may include, but is not limited to including, a buck converter, abuck-boost converter, a resonant converter, a soft switching converter,and/or a zero-voltage switching converter. In some cases, the switchmodule may include combinations of these arrangements, such as aplurality of H-bridges connected in parallel and/or series. In somecases, a switch module may be provided by a single switch, such as asingle FET switch for example. The components used for the switch modulemay be selected based on the desired current and/or voltage levels forthe particular implementation.

Each of the switches in the switch module can be configured in a closedposition or an open position. When a switch is in an open position,signals can pass through the switch. Conversely, when a switch is in aclosed position, signals cannot pass through the switch. The switchescan be actuated from an open position to a closed position or a closedposition to an open position. The switches can be any suitable type ofswitch, including, but not limited to, transistors, MOSFETs, BJTs,IBJTs, and/or thyristors.

The current flow through a switch module can depend on the particularconfiguration of the switches in that switch module. A particularconfiguration of the switches may be referred to as a switch state. Eachswitch module may be adjustable between a plurality of switch states.The plurality of sub-units states for a given generator sub-unit 220 maybe defined as the plurality of switch states for the correspondingswitch module.

As noted above, each generator sub-unit 220 can be configured to undergoa sequence of sub-unit state transitions. The sub-unit state transitionscan be defined to generate a desired sub-unit waveform, e.g. a signaturewaveform of short duration or a wavelet. The signal waveform generatedby the generator sub-unit 220 can define the signal output portion forthat generator sub-unit 220. The combined output signal generated bysignal combiner 222 may then provide a composition or superposition ofthese wavelet signature functions.

The combined output signal can be defined to provide an excitationsignal that can be applied to the load 208. Alternately or in addition,the combined output signal can be defined to provide a sensing signalthat can be applied to the load 208.

Referring again to FIG. 2A, a coupling member 207 connects the signalgenerator 206 to the load 208. The coupling member 207 may facilitatethe transfer of one or more output signals from signal generator 206 toload 208. The coupling member 207 may be implemented by variousconduits, such as a waveguide or coaxial cable, as with waveguideportion 110 of apparatus 100. Referring back to the example of apparatus100, the waveguide portion 110 defines a coupling member between thesignal generator 108 and the load defined by the transmission lineconductor portion 112.

The coupling member 207 may also be referred to as a connecting cable.The connecting cable 207 can include one or more conductors that act asone or more electrical transmission lines between the signal generator206 and the load 208.

The coupling member 207 may be considered part of the load 208. Thecoupling member 207 can include a transition region, which has a lowerimpedance relative to other regions of the coupling member 207. Thelower impedance of the transition region can result in lower voltages inthe transition region, minimizing electrical arcing that may be causedby high voltages. In some cases, the transition region can be located atthe connection between the coupling member 207 and the load 208.

The load 208 can be any component that can receive output signalsgenerated by the signal generator 206 and produce one or morepropagating, partial or full standing electromagnetic waves orexponentially decaying waves along its length. For example, the load 208can be a radiator, antenna, applicator, or lossy transmission line. Insome embodiments, the load 208 can be an inductive heating coil.

In general, the load 208 may be provided as an electromagnetic energycoupling system or radiating structure positioned within a region of thehydrocarbon medium that is to be heated. Typically, the load 208 mayinclude a lossy transmission line structure extending in a longitudinaldirection. However, various types of radiating structures may be usedfor the load 208 in different implementations.

The load 208, consisting of a radiating structure, can be positionedwithin the hydrocarbon medium. This forms a radiating structurecorridor. The radiating structure corridor can be defined as the portionof the hydrocarbon medium surrounding the load 208 (e.g. surrounding theradiating structures 208A-208C). The radiating structure corridor may bedefined as an approximately cylindrical region surrounding the radiatingstructure conductors of the load 208 (e.g. radiating structures208A-208C) that is influenced by electromagnetic heating resulting fromthe excitation signal from the generator 206. In other words, 209 mayrepresented the hydrocarbon medium payload layer in which the radiatingstructure 208 is placed forming a corridor of radiation that is roughlycylindrical in shape. This corridor of radiation may envelop theradiating structure conductors 208 and the producer pipe 122.

Referring now to FIG. 3A, shown therein is a plot illustrating exampleelectromagnetic waves 350, 352 that may be produced by the load 208. Theelectromagnetic waves 350, 352 may be produced by the load 208 whenoutput signals from the signal generator 206 are applied to the load208.

As shown in FIG. 3A, the load 208 can include one or more radiatingstructures 208A-C. The plot shown in FIG. 3A illustrates the voltage ofthe example standing waves 350, 352 along the length of the radiatingstructures 208A-C.

The radiating structures 208A-C can be connected to the signal generator(not shown), via the coupling member 207. In the example shown in FIG.3A, the radiating structures 208A-C are shown as linear structures in ahorizontal parallel arrangement. Various other geometries andarrangements of the radiating structures 208A-208C may also be used. Forexample, vertical, slanted, and unevenly spaced arrangements of theradiating structures 208A-208C may be used.

As illustrated, the voltage of the standing electromagnetic waves 350,352 can vary along the length of the load 208. The profile or shape ofthe electromagnetic waves 350, 352 may vary depending on the powerspectral density of the respective output signals.

As explained herein above, signal generator 206 can produce anexcitation signal that is coupled to the load 208. When the load 208 isexcited by the excitation signal, electromagnetic energy can be coupledinto the hydrocarbon medium 209 by the load 208.

The coupling of the electromagnetic energy into the hydrocarbon medium209 by the load 208 can take various forms. For example, the couplingmay be a resistive coupling wherein the hydrocarbon medium behaves asresistive material. Alternately, the coupling may be an inductivecoupled eddy current. Alternately, the coupling may be a lossyelectromagnetic wave. Accordingly, while the term radiating structure isused herein, it should be understood that this radiating structure cancouple electromagnetic energy into the hydrocarbon medium by generalmodes when electrically excited. In the example mode of a lossyelectromagnetic wave coupling a standing electromagnetic wave can beproduced along a length of the at least one radiating structure. In somecases, if the attenuation of the coupled electromagnetic wave is highthen there will not be a standing wave but rather an exponentiallydecaying signal strength of the electromagnetic wave along the length ofthe at least one radiating structure. In the examples described herein,such an exponentially decaying wave can also be considered as anelectromagnetic standing wave.

The electromagnetic waves 350/352 can include standing wave componentsproduced by the load 208 that correspond to the properties of theexcitation signal. For instance, the standing wave components can berelated to the power spectral density of the excitation signal generatedby the signal generator 206.

The shape of the electromagnetic waves 350, 352 may also vary based onthe properties of the hydrocarbon medium 209. The standing wavecomponents may be related to the power dissipation that occurs withinthe corridor surrounding the load 208 (e.g. the portion of thehydrocarbon medium surrounding the load 208) along the longitudinallength of the load 208. For example, the standing wave may be determinedas the square magnitude transverse field or transverse current along theradiating structure averaged over a time constant epoch. The standingwave that is present along the corridor may vary with changes in theexcitation signal and/or the nature of the load 208 and/or hydrocarbonmedium 209.

For example, when the hydrocarbon medium 209 is highly lossy, such aswhen there is a high water concentration, the voltage may decayexponentially, as illustrated by the example electromagnetic wave 350.The example electromagnetic wave 350 shown in FIG. 3A has the form of anattenuated forward propagating wave. Conversely, when the hydrocarbonmedium 209 is not highly lossy, such as when there is a low waterconcentration, the output signals applied to the load 208 can propagateand partially reflect back and forth along the load 208. The reflectionscan result in an electromagnetic wave having a partial standing wavepattern, as illustrated by the example electromagnetic wave 352.

The example electromagnetic wave patterns 350, 352 may include one ormore propagating wave components. The propagating wave components mayinclude significant reflections from both the proximal and distal endsof the radiating structure. In some cases, changes of theelectromagnetic properties along the radiating structure may also resultin wave reflections. The electromagnetic wave patterns 350, 352 may alsobe time-varying. That is, the position of the peaks and troughs of theelectromagnetic field density can change over time and/or can changewith frequency of generator excitation. For example, the standing wavepatterns 350, 352 may be varied over time by modulating the outputsignals applied to the load 208.

The electromagnetic wave patterns 350, 352 correspond to spatial heatingprofiles averaged over the radiating corridor along the length of theload 208. For example, less heat may be generated in the low voltageregions 352A than in the high voltage regions 352B of theelectromagnetic wave 352. Accordingly, the spatial heating profile canbe controlled by controlling the wave profile along the load 208, i.e.by controlling the characteristics of the output signals applied to theload 208.

The spatial heating profile may be adjusted to increase the efficiencyof hydrocarbon heating. For example, the spatial heating profile may beadjusted to minimize heating in areas of the hydrocarbon medium 209expected to provide inefficient oil production. For example, heating maybe minimized in areas that have already produced oil, or in areasassociated with poor pay zones that may not be economic (e.g. monetarilyor energy-wise) to produce. For example, the spatial heating profile canbe configured to focus power to regions where hydrocarbon has not yetbeen sufficiently extracted, and minimize heating in areas that aredepleted or where the formation has poor initial hydrocarbon saturation.The spatial heating profile may also be configured to minimize highvoltage regions (or “hot-spots”) that may result in electrical arcingand potential equipment damage.

Referring back to FIG. 2A, the load 208 can have a frequency-dependentimpedance. That is, the impedance experienced by a signal applied to theload 208 may depend on the frequency of the applied signal.

The generator 206 can be configured to produce an excitation signal thatis connected to the load 208. The coupling between the generator 206 andload 208 may depend on the frequency of the excitation signal generatedby the generator 206. That is, the impedance of the load 208 may befrequency dependent and the impedance of the load 208 may vary based onthe frequency of the excitation signal generated by the generator 206.Accordingly, coupling between the generator 206 and the load 208 may beadjusted by controlling the attributes of the excitation signal producedby generator 206 such as the frequency of the excitation signal.

The coupling between the generator 206 and load 208 affects the abilityof the load 208 to couple heat into the medium 209. Accordingly, thecoupling is a component of the overall heating life cycle.

The frequency-dependent impedance of the load 208 may depend on theelectromagnetic properties of the hydrocarbon medium 209 surrounding theload 208 (i.e. the radiating structure). The mechanical configuration ofthe load 208 includes, for example, the geometry of the load 208.

The frequency-dependent impedance of the load 208 may also be affectedby the environment in which the load 208 is positioned. For example, theimpedance of the load 208 can be affected by the material composition ofthe hydrocarbon medium 209.

Alternately or in addition, the load 208 can have an inputtime-dependent impedance. For instance, the input impedance of the load208 may change as the electromagnetic properties of the hydrocarbonmedium changes over time due to heating of the hydrocarbon medium 209.For example, the concentration and distribution of water in thehydrocarbon medium 209 may change over time. This may result in changesto the electromagnetic properties of the load 208 and, in turn, theinput impedance of load 208.

The impedance of the load 208 can also vary based on the amplitude ofthe excitation signal produced by the generator 206. In some cases, theimpedance of the load 208 may vary nonlinearly with respect to theamplitude of the excitation signal.

The load 208 can be implemented using a variety of geometries andvarious physical dimensions. As illustrated in FIGS. 3A and 3B, load 208has a longitudinal axis, and the extent of the load 208 along thelongitudinal axis can define the length of the load. In the example ofFIG. 3B, the longitudinal axis extends in the longitudinal direction 326between a proximal end 322 (proximate to the generator 206 and/orcoupling member 207) and a distal end 324 (spaced apart from thegenerator 206 and coupling member 207). The length of the load 208 canbe defined so that small changes in the power spectral density of outputsignals applied to the load 208 can result in large changes in thepattern of the produced standing electromagnetic wave.

In some embodiments, the load 208 can include an arrangement of multipleelements, such as a group of radiators. For example, the load 208 mayinclude one or more radiating structures positioned in the hydrocarbonmedium 209, such as radiating structures 208A-208C shown in the exampleof FIG. 3 and the transmission line conductors 112 a and 112 b shown inthe example of FIG. 1 . When output signals are applied to the radiatingstructures, a standing electromagnetic wave can be produced along alength of the radiating structures and electromagnetic energy isradiated into the hydrocarbon formation.

As shown in the example of FIG. 1 , the radiating structures may includea plurality of transmission line conductors 112 including firsttransmission line conductor 112 a and a second transmission lineconductor 112 b. The signal generator 206 may then generate a firstoutput signal to be applied to the first transmission line conductor 112a and a second output signal to be applied to the second transmissionline conductor 112 b.

The second output signal may be a phase shifted version of the firstoutput signal. That is, the second output signal may include the firstoutput signal with the addition of a phase shift. For example, thesecond output signal can be the first output signal with a 180° phaseshift. As a result, the first transmission line conductor and the secondtransmission line conductor can have electrically different lengths.

The load 208 can include various components (not shown) that can beconfigured to vary the standing electromagnetic waves produced along itslength. For example, load 208 may include one or more generator signalexcitation components that can be configured to modify the spatialfrequency, voltage, current, power, phase, and/or other property of thestanding electromagnetic waves. The load 208 may also include components(not shown) that can be configured to vary the load impedance (orresistance or reactance) of the load 208. In some cases, more than oneconfiguration of the load 208 may result in the same standingelectromagnetic waves and/or load impedance.

In some embodiments, the load 208 can include a sacrificial material.The sacrificial material may be applied to an outer surface of the load208 to provide a sacrificial layer. The sacrificial layer can protect aconductive surface of the load 208 from damage caused by electricalarcing and/or corrosion. This may help maintain the electricalconnection between the signal generator 206 and the load 208.

The controller 202 can control the various components of theelectromagnetic heating control system 200, such as the signal generator206 and the load 208. The controller 202 can determine control settingsto be applied to one or both of the signal generator 206 and the load208. For example, the controller 202 may control characteristics of theoutput signals (e.g., the power spectral density) generated by thesignal generator 206. The controller 202 may adjust control settings ofone or both of the signal generator 206 and the load 208 to definedesired spatial heating profiles along the load 208. As used herein, theterm control settings may also be understood to include configurationsettings.

The controller 202 may be implemented using any suitable processor,controller or digital signal processor that provides sufficientprocessing power depending on the configuration, purposes andrequirements of the electromagnetic heating control system 200. In someembodiments, the controller 202 can include more than one processor witheach processor being configured to perform different dedicated tasks.The controller 202 may be implemented in software or hardware, or acombination of software and hardware. Although the controller 202 isshown as one component in FIG. 2A, in some embodiments, the controller202 may be provided by one or more components distributed over ageographic area and connected via a network.

In some embodiments, the controller 202 may include a storage component(not shown). The storage component can include RAM, ROM, one or morehard drives, one or more flash drives or some other suitable datastorage elements such as disk drives, etc. The storage component canstore data in various databases or file systems. For example, thestorage component may store data usable with a predictive model 204, amodel parameter generator 216, a heating life-cycle sub-unit 230, acontrol setting generator 218 and/or various other components of system200.

The controller 202 can transmit and receive data signals to and fromother devices, including the various components of the electromagneticheating control system 200. For example, the controller 202 may receiveinformation regarding the hydrocarbon medium 209 from various systemcomponents such as data sources 212 and/or sensors 210. The control 202may transmit control settings to various system components such assignal generator 206 and/or load 208.

As shown in the example of FIG. 2A, controller 202 may include apredictive model 204, a model parameter generator 216, and a controlsetting generator 218. It will be appreciated that these components areshown to illustrate example functionalities of the controller 202, andare not intended to be restrictive. In some embodiments, thesecomponents may implemented in different ways, including being combinedinto fewer components, or divided into additional components.Furthermore, the controller 202 may include additional components thatare not shown in FIG. 2A, such as a life cycle sub-unit 230 (see e.g.FIGS. 2C and 2E) for example.

FIG. 2C illustrates an example of a life cycle sub-unit 230 that may beprovided by controller 202 and/or an external processor coupled tocontroller 202. The life-cycle sub-unit 230 may be configured to definea desired heating life cycle for the system 200. The desired heatinglife cycle can be used to determine a desired operational state of thesystem 200. The desired operational state can be provided to the controlsetting generator 218 (see e.g. FIG. 2E). The desired operational statecan be used by the control setting generator 218 to determine one ormore desired signal generator control settings for the signal generator208. The one or more desired signal generator control settings can beselected to provide the desired operational state.

The life-cycle sub-unit 230 can be configured to define a desiredheating life cycle for the operational lifespan of the electromagneticheating provided by system 200. The desired heating life-cycle may bedefined based on characteristics of system 200, hydrocarbon medium 209,and the interaction between various components of system 200 and medium209. The desired heating life-cycle can be defined to include variousdesired heating characteristics along the load corridor or radiatingstructure corridor such as a desired spatial heating profile. Thedesired heating characteristics such as the desired spatial heatingprofile can be used to determine a desired electromagnetic wave patternto be generated in the corridor. The control setting generator 218 canthen determine the desired signal generator control settings expected toprovide the desired electromagnetic wave pattern.

The life cycle sub-unit 230 can be configured to determine a desiredheating life cycle based on data from a plurality of data sources. Asshown in the example of FIG. 2C, the life cycle sub-unit 230 can becoupled to data sources including a life cycle database 232, apredictive model 204, and one or more sensors 210. The life cyclesub-unit 230 can use the data received from the data sources in order todefine the desired heating life cycle.

The production life cycle database 232 can be configured to include datarelated to an expected life cycle model. In some cases, the life cycledatabase 232 may include data related to the heating life cycle ofhydrocarbon mediums or formations that have previously undergoneelectromagnetic heating. The life cycle database 232 can also includedata related to the components of system 200, such as the known and/orexpected characteristics of signal generator 206, coupling member 207,load 208, and hydrocarbon medium 209.

As described in further detail herein, predictive model 204 can beconfigured to determine a predicted/simulated behavior of the signalgenerator 206, the load 208, and/or the hydrocarbon medium 209 inresponse to an existing status (either expected or actual) of propertiesof the system 200. The life cycle sub-unit 230 may be configured todefine an initial desired heating life cycle for the medium 209 usingpredictive model 204 with data from life cycle database 232.

The life cycle sub-unit 230 can also be configured to adapt/update thedesired heating life cycle based on feedback from components of system200, such as sensors 210 and/or generator 208. For example, feedbackfrom the sensors 210 may indicate that the actual or current heatingprofile in the corridor differs from the initial desired heating lifecycle for the medium 209. Accordingly, the life cycle sub-unit 230 canbe configured to update the desired heating life cycle to account forthese differences. The updated desired heating life cycle may be definedin a similar manner to the initial desired heating cycle. As with theinitial desired heating life cycle, the updated desired heating lifecycle can be defined to provide an optimized sequence of heatingprofiles based on the actual status of medium 209 and/or system 200and/or a predicted status generated by predictive model 204. The desiredheating life cycle may be defined to maximize the efficiency of thereservoir heating, and associated hydrocarbon extraction, within theoperational constraints of system 200. The updated desired heating lifecycle may then be provided to control setting generator 218 to be usedin determining control settings for signal generator 206.

The control settings can then be applied to signal generator 206 inorder to define the excitation signal produced. This excitation signalcan then be applied to the load 208 in order to generate anelectromagnetic wave within the corridor. As a result, the updatedcontrol settings can cause changes in the predictive model 204 for themedium 209 and the system 200 as a whole. The change in theelectromagnetic wave can also be identified through feedback fromsensors 210 monitoring the medium 209 and/or components of system 200such as the signal generator 206. This feedback can be provided to thelife cycle sub-unit 230 to further update the desired heating life cycleas required.

Referring again to FIG. 2A, the predictive model 204 may provide arepresentation of at least some of the components of the electromagneticheating control system 200. For example, the predictive model 204 can beused to determine a predicted/simulated behavior of the signal generator206, the load 208, and/or the hydrocarbon medium 209 in response to anexisting status (either expected or actual) of the system 200.

The predictive model 204 can be used to simulate interactions betweenthe various components of system 200. The predictive model 204 maydetermine expected electromagnetic, thermal, fluid, or structuralproperties of system 200. For example, the predictive model 204 maydetermine expected electromagnetic standing waves generated by the load208, the temperature profile of the hydrocarbon medium 209, and the flowof water or hydrocarbons within the hydrocarbon medium 209 based on anexisting status of the system 200 and/or the control settings of system200.

In general, predictive model 204 can be used to predict the status ofvarious properties of the electromagnetic heating control system 200based on model parameters. The model parameters can be inputs to thepredictive model 204, which are used by the predictive model 204 tosimulate a current operational status of the parameters of the system200.

Some of the model parameters may reflect observable/measurableproperties of the hydrocarbon medium, the load, and/or the signalgenerator. For example, the relative permittivity (or dielectricconstant) of the hydrocarbon medium 209 may be used as a modelparameter. Other examples of model parameters can include one or more ofthe temperature, pressure, water concentration, current, voltage,impedance, and frequency of one or more of the hydrocarbon medium, theload, and the signal generator.

In some cases, the actual status of the model parameters correspondingto observable/measurable properties may be determined using measureddata from the data sources 212 or the sensors 210. This may allow thepredictive model 204 to determine the current operational status of thesystem 200 using the actual characteristics of the system 200 at thepresent time.

The predictive model 204 may also use an expected status of one or moremodel parameters to determine the current operational status of thesystem. In some cases, the expected status of one or more modelparameters can be determined by the model parameter generator 216. Theexpected status of a model parameter may be used, for example, where theactual status is not currently available, e.g. due to the unavailabilityof the actual status or the intermittent availability of the actualstatus.

In some cases, some model parameters may be difficult, impractical, oreven impossible to directly observe. For example, it may be impracticalto directly measure certain properties of particular regions of thehydrocarbon medium 209 because they are positioned deep underground, faraway from the surface. Furthermore, in some cases, sensors 210 may beexpensive or fragile to install. In such cases, the predictive model 204may rely on an expected status of these properties in determining thecurrent operational status of the system 200. The predictive model 204may also use available observable data that can be used to infer thecurrent operational status of the system 200. In some cases, thepredictive model 204 may update the expected status to account for thecomplete set of past and current observables measured in a Bayesianprobabilistic sense.

In some cases, the predictive model 204 may be implemented using asimplified model of the load 208 and its electromagnetic interactionwith the hydrocarbon medium 209. This may reduce the number of modelparameters required and/or reduce the computational intensity of thepredictive model 204.

Alternately, the predictive model may be defined to use a more complexmodel of the system 200. A more complex model may include additionalsystem characteristics such as configuration properties of the load 208,dielectric properties of the hydrocarbon medium 209, temperatures alongthe load 208, concentrations of water and hydrocarbon in the hydrocarbonmedium 209, a likelihood of electrical arcing and so forth. This mayallow the model 204 to provide a more accurate representation of theelectromagnetic heating control system 200, which may promote morerefined control.

The predictive model 204 may include a wave model of the electromagneticstanding wave generated by the load 208. The modeled electromagneticstanding wave can be used to determine temperatures of the hydrocarbonmedium 209. This may also allow the predictive model to estimate theflow of water and hydrocarbons within the hydrocarbon medium 209.

In some embodiments, the predictive model 204 may model theelectromagnetic standing wave based on the propagation of the outputsignals along the load 208 and the resultant electromagnetic fields. Insome cases, the output signals may be modeled as propagating inapproximately transverse electromagnetic mode (TEM). For example, theoutput signals may include a lossy guided electromagnetic propagatingmode that may be approximately represented as transverse electromagneticmode. That is, the output signals can be modeled as having anelectromagnetic field pattern that is approximately perpendicular ortransverse to the direction of propagation. This approach may besuitable where the separation distances between radiating structures ofthe load 208 do not abruptly change, and where the wavelengths of theoutput signals are significantly longer than the transverse dimensionsof the load 208. Modeling the propagation of the output signals as beingsubstantially TEM may reduce the computational complexity of thepredictive model 204 and may reduce the number of model parameters. Insome embodiments, the output signals may also be modeled as having anexponential decay along the load 208. The exponential decay mayrepresent conductor losses of the load 208 and dielectric losses of thehydrocarbon medium 209.

A number of different modeling techniques may be used to implement thepredictive model 204. FIG. 8A illustrates a simplified example of thepredictive model 204 in which the load 208 may be treated as a pair ofparallel cylindrical pipes positioned with the hydrocarbon medium 209.This example implementation of predictive model 204 may consider aportion of the load 208 and the hydrocarbon medium 209, due to theirsymmetry, to reduce computational complexity. For example, thepredictive model 204 may only consider a quarter 802 of a cross-sectionof the load 208 and the hydrocarbon medium 209

The predictive model 204 may assume approximately transverseelectromagnetic mode of transmission (TEM) and hence model theelectromagnetic standing waves based on Laplace's equation: ∇²ϕ(x, y)=0,where ϕ represents the electric potential in a transverse plane of theradiating structure of 112. For example, the predictive model 204 mayseta boundary condition ϕ(x, y)=1 at the surface of one pipe, and ϕ(x,y)=−1 at the surface of the other pipe. The predictive model 204 canfurther can set x=0 as an equipotential surface, and y=0 as a Neumanncondition. Alternatively, more accurate models can be developed assumingthe presence of all 6 spatial components of electromagnetic fields (fullwave). However, there are various other models of the electromagneticpropagation which may be better suited depending on the electromagneticproperties of the hydrocarbon medium corridor. For instance, thediffusion of water around 112 will have a significant impact on theelectromagnetic wave field structure that may not be well approximatedby the TEM.

The predictive model 204 may incorporate a number of assumptions orestimates regarding the nature of the hydrocarbon medium 209. Forexample, the predictive model 204 may be defined to model thehydrocarbon medium 209 with no free charge within the hydrocarbon medium209, such that ∇E=0. Additionally or alternatively, the predictive model204 may be defined to model the hydrocarbon medium 209 as beinghomogenous, isotropic, and linear, such that D=εE. Additionally oralternatively, the predictive model 204 may be defined to model thehydrocarbon medium 209 with a non-zero current, but with no accumulatedcharge in the hydrocarbon medium 209. Additionally or alternatively, thepredictive model 204 may be defined to model the hydrocarbon medium 209to have no time variation. Additionally or alternatively, the predictivemodel 204 may be defined to model the hydrocarbon medium 209 such thatthe electric field (E-field) and magnetic field (H-field) are notcoupled.

The predictive model 204 may be configured to determine the electricfield and the magnetic field within the hydrocarbon medium 209. Forexample, the predictive model 204 may determine the electric potential,ϕ(x, y), and then determine the electric field, E=−∇ϕ(x,y); the currentflow, J=σE=−σ∇ϕ(x,y); and the magnetic field, V×H=J.

In some cases, the predictive model may be defined to model thehydrocarbon medium 209 to be time variant. The predictive model 204 maythen be implemented using time variant equations for the magnetic andelectric fields, such as

${{\nabla \times H} = {J + {\varepsilon\frac{dE}{dt}}}}{and}{{{\nabla \times E} = {{- \mu}\frac{dH}{dt}}},}$

and the E and H fields may be coupled.

If the output signals are TEM, the E and H transverse fields are thesame as the static solutions (i.e. the same as the time invariant model)the E and H field components are proportional with a constant of themode impedance, and the electric potential ϕ(x, y) can still be used.

FIG. 8A illustrates a simplified example of the predictive model 204 inwhich the regions of the hydrocarbon medium 209 near the load 208 aremodelled as an inner dry region 209A and an outer wet region 209B. Thedry region 209A may be modelled as a dielectric layer. This may resultin the model including a boundary region of bound charge, where ∇²E≠0,which can result in a non-TEM standing electromagnetic wave. Thepredictive model 204 may model the dry region 209A to have a radius thatincreases over time.

The predictive model 204 may assume that the electric field issymmetrical and orthogonal to the surface of the load 208.

The model 204 may define the boundary between the dry region 209A andthe wet region 209B as an equipotential surface.

The model 204 may define the inner region 209A as a coaxial cable. Thecoaxial cable may have a capacitance per unit length,

${c = \frac{2{\pi\varepsilon}}{\ln( \frac{b}{r_{p}} )}},$

where r_(p) is me pipe radius and b is the dielectric boundary radius.The capacitance per unit length of the load 208 can be determined as,

$C = \frac{1.36\varepsilon}{\log_{10}( {( \frac{h}{b} ) + \sqrt{( \frac{h}{b} )^{2} - 1}} )}$

and the conductance can be determined as,

${G = \frac{1.36\rho}{\log_{10}( {( \frac{h}{b} ) + \sqrt{( \frac{h}{b} )^{2} - 1}} )}},$

where 2 h is the distance between the two pipes.

FIG. 9A illustrates an example of an equivalent circuit 900A that may beused with the predictive model shown in FIG. 8B. In the equivalentcircuit 900A, C_(p) may correspond to the dry region 209A and C and Rmay correspond to the wet region 209B. FIG. 9B illustrates an example ofa simplified equivalent model 900B that may be determined from theequivalent circuit 900A.

The simplified model 900B may be defined according to

${\begin{bmatrix}V_{1} \\I_{1}\end{bmatrix} = {\begin{bmatrix}a & b \\c & d\end{bmatrix}\begin{bmatrix}V_{2} \\I_{2}\end{bmatrix}}},$

where

${{a = 1},{b = {sL}},{c = \frac{1}{z}},{d = {\frac{sL}{z} + 1}},{and}}{z = {\frac{1}{{sC}_{p}} + {\frac{R/2}{{RCs} + 1}.}}}$

It can be determined that

${{c = \frac{1}{\sqrt{\varepsilon_{0}\mu_{0}}}},{c = \frac{1}{\sqrt{2C_{0}L}}},{where}}{{C_{0} = \frac{1.36\varepsilon_{0}}{\log_{10}( {( \frac{h}{r_{p}} ) + \sqrt{( \frac{h}{r_{p}} )^{2} - 1}} )}},{and}}{L = {\frac{1}{c^{2}2C_{0}}.}}$

The model can be defined to assume that the end of the radiatingstructure conductor 112, has a relatively high impedance, and the modelcan thus estimate the current and voltage at the end of the radiatingstructure conductor to be:

$\begin{bmatrix}V_{end} \\I_{end}\end{bmatrix} = \begin{bmatrix}1 \\0\end{bmatrix}$

The model can be defined to determine the voltage and current at adistance of one meter from the end of the pipe using:

${\begin{bmatrix}V_{1} \\I_{2}\end{bmatrix} = {\begin{bmatrix}a & b \\c & d\end{bmatrix}\begin{bmatrix}V_{end} \\I_{end}\end{bmatrix}}},$

and to determine the voltage and current n meters from the end of thepipe recursively using:

$\begin{bmatrix}V_{n} \\I_{n}\end{bmatrix} = {{\begin{bmatrix}a & b \\c & d\end{bmatrix}\begin{bmatrix}V_{n - 1} \\I_{n - 1}\end{bmatrix}}.}$

The predictive model 204 can determine the value of one or more modelparameters based on equivalent circuit component values using variousanalytic techniques, such as finite element analysis for example.Examples of model parameters that may be determined by the predictivemodel can include electric potential, ϕ(x, y), field energy E_(e),capacitance C, where

${E_{e} = {\frac{1}{2}{CV}^{2}}},$

and V represents voltage, inductance

${L = \frac{\varepsilon_{e}}{{Cc}^{2}}},$

lossless characteristic impedance

${Z_{TEM} = \sqrt{\frac{L_{TEM}}{C_{TEM}}}},$

shunt conductance (e.g. based on the shunt current), shunt current (e.g.based on the electrical field and current density, J_(c)=σE andJ_(d)=ωε₀ε_(r)E), propagation constant γ=√{square root over((R+jωL_(TEM))(G+jωC_(TEM)))}. In some embodiments, the model 204 mayconsider R to be negligible (e.g. where the load 208 is cladded) and thepropagation constant can be determined according to γ=√{square root over(jωL_(TEM)(G+jωC_(TEM)))} with γ=α+jβ.

Referring again to FIG. 2A, the predictive model 204 can also include amedium heat transfer model representing an estimation of heat transferwithin the hydrocarbon medium 209. The medium heat transfer model mayinclude multiple different mechanisms of heat transfer in thehydrocarbon medium 209.

For example, the medium heat transfer model can include a first heattransfer model and a second heat transfer model. The first heat transfermodel may represent an approximation of heat transfer within thehydrocarbon medium 209 when there is a high concentration of water inregions of the hydrocarbon medium 209 near the load 208. The first heattransfer model may reflect heat transfer that is caused primarily byconductive currents, resulting in free electron motion. In the firstheat transfer model, the heat transferred may be proportional to thesquare magnitude of the electrical field.

The second heat transfer model may represent an approximation of heattransfer within the hydrocarbon medium 209 when there is a low waterconcentration in regions of the hydrocarbon medium 209 near the load208. The second heat transfer model may reflect heat transfer that iscaused primarily by the movement of bound charges caused by a changingelectrical field. In the second heat transfer model, the heattransferred may depend on the frequency of the electromagnetic standingwaves generated by the system 100.

The model parameter generator 216 can be used to determine thestatus/value of model parameters that may be used by the predictivemodel 204. In some embodiments, the model parameter generator 216 maydetermine the actual status of the model parameters used by thepredictive model 204. For example, the model parameter generator mayreceive a measured value of the status of a model parameter from datasources 212 and/or sensors 210.

In some cases, each model parameter may be linked with at least oneobservable/measurable data reference. Accordingly, the status of thatparameter in the model may be updated in a Bayesian sense.

For example, the model parameter may be a temperature value. The modelparameter generator 216 may receive a measured temperature signal from atemperature sensor 210 representing the actual status of the temperatureas it was measured by the sensor. The model parameter generator 216 maythen use the actual status of the model parameter in the predictivemodel 204.

In some embodiments, the model parameters may be determined by the modelparameter generator 216 based on data received from the data sources 212or the sensors 210. For example, the model parameter generator 216 maydetermine the value of a model parameter based on the measured status ofa related model parameter (e.g. determining the current based on ameasured voltage across a known/estimated resistive value).

In some embodiments, the model parameter generator 216 may be configuredto estimate the value of one or more model parameters. For example, themodel parameter generator 216 may use a Bayesian tracking or expectationmaximization technique to determine the status of one or more modelparameters. Alternately or in addition, the model parameter generator216 may use a Kalman filter technique to determine the status of one ormore model parameters. Alternately or in addition, the model parametergenerator 216 may use a machine learning model to determine the statusof one or more model parameters. For example, an artificial neuralnetwork may be trained to generate an estimated status of one or moremodel parameters based on inputs from the data sources 212 and/or thesensors 210 and/or previous values determined by the predictive model204 and/or model parameter generator 216.

Referring now to FIG. 2D, shown therein is a block diagram of an exampleprocess for generating model parameters. The example process forgenerating model parameters shown in FIG. 2D is an example of a processthat may be implemented by the model parameter generator 216. Theprocess shown in FIG. 2D can be used to generate updated modelparameters that can be used by the predictive model 204 to evaluate howchanges to the control settings may impact the system 200 and inparticular the heating profile within medium 209.

As shown in FIG. 2D, the model parameter generator 216 can be configuredto implement a plurality of sub-models. As shown in the example of FIG.2D, the model parameter generator can incorporate a corridor model 240,a generator model 242, and a sensor prediction model 244.

In some examples, corridor model 240 can be defined using a predictiveparameterized model of the radiating structure corridor. The radiatingstructure corridor may be defined as a cylindrical region surroundingthe conductors of the load 208 (e.g. radiating structures 208A-208C).The boundaries of the cylindrical region can be defined to include theportion of the medium 209 that is influenced by electromagnetic heatingresulting from the excitation signal applied to load 208 from thegenerator 206. The corridor model 240 can be defined to represent theelectromagnetic properties of the hydrocarbon medium 209 within theradiating structure corridor that is affected by the electromagneticheating caused by excitation of the load 208.

In some cases, the corridor model 240 may be defined using structuralapproximations of the radiating structure corridor. The corridor model240 can be defined to represent electromagnetic propagation along theradiating structure (e.g. load 208) using a plurality of propagationsub-models. The corridor model 240 can be defined to also represent thewater, steam flow and temperature profile along the radiating structurethat may change with time.

FIG. 3B illustrates an example corridor model that is defined using twopropagation sub-models. The corridor model illustrated in FIG. 3B may beused, for example, to implement the corridor model 240 shown in FIGS. 2Dand 2E. A first propagation sub-model can be defined as a transversepropagation model. A second propagation sub-model can be defined as alongitudinal propagation model. The corridor model can then be definedas a product of the transverse propagation model and the longitudinalpropagation model.

As shown in FIG. 3B, the corridor within the medium 209 can be dividedinto a plurality of longitudinal slices or sections 320A-320N. Eachlongitudinal section 320 may be defined to include a specified length ofthe corridor in the longitudinal direction 326. The specified length foreach section 320 may be defined to be significantly smaller than thewavelength of the highest frequency component of the power spectraldensity achievable by generator 206. For example, each section 320 maybe several meters in length in the longitudinal direction 326.

The transverse propagation sub-model can be configured to be applied toeach longitudinal section. That is, each longitudinal section may beindividually modelled using the transverse propagation sub-model. Thetransverse model can be configured to estimate the status of materialproperties of the hydrocarbon medium 209 that affect the dielectricproperties. The transverse propagation sub-model in each section canprovide an estimated status of electromagnetic properties of eachlongitudinal section such as water concentration, water vapor creation,water vapor condensation, heat flow, and hydrocarbon concentration forexample. The estimated status of the material properties can then beused to estimate the average value of the medium dielectric for eachsection. The average dielectric value for each section can then be usedto determine the overall section dielectric property and mode impedance.

The longitudinal sub-model can be configured to represent thetransmission line mode and longitudinal standing wave pattern generatedby the load 208. The longitudinal sub-model can be defined to provide arepresentation of the standing wave pattern for the entire load 208,based on the estimated status of properties determined by the transversepropagation sub-model. The longitudinal model can be configured todetermine the longitudinal mode and power dissipation in each section320, based on the status of the dielectric properties determined by thetransverse sub-model. The determined dissipation can then be used toupdate the status of the enthalpy and hence temperature in each section320.

The transverse propagation sub-model and longitudinal sub-model can beconfigured to operate iteratively. The outputs from the transversepropagation sub-model can be used to update the longitudinal sub-model.Similarly, the outputs from the longitudinal sub-model can be used toupdate the transverse propagation sub-model.

The corridor model 240 can output the estimated status of the variousproperties as model parameters 246. The model parameters 246 can beprovided to the sensor prediction model 244 for use in estimating thestatus of various properties of the signal generator 206, the load 208,and/or the hydrocarbon medium 209.

The generator model 242 can be configured to estimate the properties ofthe excitation signal produced by generator 206 in response to thegenerator control settings 248 provided by the control setting generator218. The generator model 242 can be configured based on thecharacteristics of the generator 206 as well as models of expectedchanges in generator operations over time (e.g. changes expected due towear and tear on the generator 206). The generator model 242 cangenerate an estimated excitation signals that can be provided to thesensor prediction model 244.

The sensor prediction model 244 can be configured to estimate the statusof various measurable properties of the signal generator 206, the load208, and/or the hydrocarbon medium 209 in response to the current stateof the system 200 (based on the model parameters 246) and the estimatedexcitation signal received from generator model 242. The sensorprediction model 244 can be configured to predict the status of sensedproperties that may be collected by sensors 210 as well as propertiesderivable from the sensor data.

As shown in FIG. 2D, the sensor prediction model 244 can be coupled tothe sensors 210. The predicted status of the one or more properties canbe compared to actual or measured status received from sensors 210. Thesensor prediction model 244 can then generate one or more error values245 representing the difference between the predicted status of the oneor more properties and the actual status of the one or more properties.The error value(s) 245 can be provided to corridor model 240. Corridormodel 240 can use the error value(s) 245 to update the corridor model240 to account for the differences between the predicted and actualstatus of the signal generator 206, the load 208, and/or the hydrocarbonmedium 209. The error value(s) 245 and/or measured status of one or moreproperties can also be provided to the corridor model 240 in order toupdate the model parameters 246 based on the actual measured status ofthe properties of the signal generator 206, the load 208, and/or thehydrocarbon medium 209. The model parameters 246 generated by thecorridor model 240 can be used to further determine any adjustments thatmay be necessary to the heating profile within the medium 209 (e.g. toupdate the desired heating life cycle), and in turn the necessarymodifications to the excitation signal generated by generator 206.

The control setting generator 218 can be configured to determine andapply control settings to various components of the electromagneticheating control system 200. The control setting generator 218 candetermine the control settings to be applied based on expectedoperational responses determined by the predictive model 204.

For example, the predictive model 204 may predict the effect anddesirability of particular control settings. The control settinggenerator 218 may use the predicted results of multiple differentpossible control settings to select a particularly optimized set ofcontrol settings.

The control setting generator 218 and predictive model 204 may apply aconstrained optimization to determine the control settings. An exampleblock diagram of an overall process for determining the signal generatorcontrol settings is shown in FIG. 2E. The setting determination processillustrated in FIG. 2E may be implemented by various components ofsystem 200, such as controller 202 and sensors 210. As shown in FIG. 2E,components of system 200 such as the predictive model 204 and controlsetting generator 218 can be configured to perform an iterative processto optimize the operational state of the system 200.

As shown in FIG. 2E, the predictive model 204 can be configured toinclude a corridor model 240, generator model 242 and response predictormodel 250. Although shown as separate components, it should beunderstood that corridor model 240, generator model 242 and responsepredictor model 250 may be provided as separate components or as anintegrated predictive model.

The predictive model 204 can be configured to determine a predictedresponse of system 200 based on a potential set of signal generatorcontrol settings received from control setting generator 218. Thepotential set of signal generator control settings may be defined basedon a potential operational state. The potential set of signal generatorcontrol settings can be provided to the generator model 242. Thegenerator model 242 can then determine estimated properties of theexcitation signal produced by generator 206 and applied to load 208 inresponse to the generator control settings 248 provided by the controlsetting generator 218.

The generator model 242 can provide the estimated excitation signalproperties to the response predictor model 250. The response predictormodel 250 can also receive model parameters 246 from the corridor model240. The model parameters 246 may be generated using a model parametergeneration process such as that shown in FIG. 2D and described herein.The response predictor model 250 can be configured to determine apredicted response of the system 200 and medium 209 based on theestimated excitation signal properties and the received modelparameters. The predicted response may include a predicted heatingcharacteristics for the corridor around the load 208, such as apredicted spatial heating profile.

The control setting generator 218 can be configured to evaluate one ormore potential sets of signal generator control settings to determinethe signal generator control settings to apply to signal generator 206.As shown in the example of FIG. 2E, the control setting generator 218can include a potential setting generator 252 and a control settingoptimizer 254. The potential setting generator 252 can be configured todetermine one or more potential sets of signal generator controlsettings that can be applied to signal generator 206. The potential setsof signal generator control settings may specify signal generatorcontrol settings such as power levels, signal frequency/frequencies, apossible sequence of state transitions etc. The potential sets of signalgenerator control settings may be determined based on constraints of thesignal generator 206 (e.g. the different settings available) as well asadditional constraints that may be defined for the controller 202, suchas acceptable power levels for example.

The control setting optimizer 254 can be configured to evaluate aplurality of potential sets of signal generator control settings toidentify the set of signal generator control settings to apply to signalgenerator 206. The control setting optimizer 254 can be configured toperform a constrained optimization of the fitness of the potential setsof signal generator control settings with the desired heating lifecycle. As noted above, a predicted response of the generator and theradiating structure corridor can be determined by the predictive model204 for each potential set of signal generator control settings definedby the potential setting generator 252.

The control setting optimizer 254 can be configured to determine aplurality of potential operational states based on the data receivedfrom predictive model 204. The control setting optimizer 254 may compareeach potential operational states with the desired heating life cycledefined by life cycle sub-unit 230. The control setting optimizer 254can be configured to evaluate a fitness of each potential operationalstate (and thus the corresponding set of signal generator controlsettings) with the desired heating life cycle. The control settingoptimizer 254 can then identify the desired operational state (and inturn the corresponding desired signal generator control settings) as thepotential operational state that maximizes the fit between theoperational state and the desired heating life cycle.

The fit may be considered a generalized multi-component objective with aplurality of optimizable components. The heating life cycle may beconsidered a component of the production life cycle which can includeany and all aspects of the process of extracting hydrocarbons from ahydrocarbon medium including the well planning, installation, heating,production and capping for example.

For example, the control setting optimizer 254 can be configured todetermine a cost (e.g. a potential cost penalty) associated with eachpotential operational state. The cost may represent a difference ordistance between the potential operational state and the operationalstate defined by the desired heating life cycle. The control settingoptimizer 254 may determine a minimum cost operational state of theplurality of potential operational states by identifying the potentialoperational state associated with a lowest cost penalty of the pluralityof cost penalties. The control setting optimizer 254 may then identifythe minimum cost operational state as the desired operational state. Thecontrol setting optimizer 254 can then select the potential set ofsignal generator control settings corresponding to that operationalstate as the signal generator control settings to be applied to signalgenerator 206.

Various cost factors may be included in the optimization/costminimization process performed by the control setting optimizer 254. Forexample, various cost factors such as energy loss, energy efficiency,overall power dissipation, generator power loss, high voltage risk, highcurrent risk, overheating risk, soft switching performance and so forth.The control setting optimizer 254 can be configured to weigh the variousfactors for each potential operational state to determine theoperational state provided the maximum fit with the desired heating lifecycle while satisfying operational constraints of the generator 206 andsystem 200 as a whole.

Referring back to FIG. 2A, control setting generator 218 may use amachine learning model (that can be defined with one or more constrainedparameters) to determine desired control settings. For example, anartificial neural network may be trained to generate control settingsbased on particular inputs, such as predictions from the predictivemodel 204, and/or input data from the data sources 212 and/or sensors210.

Optionally, the controller 202 may evaluate the reliability of modelparameters generated by the model parameter generator 216. Thereliability may represent an evaluation of the accuracy of the modelparameters generated by the model parameter generator 216.

Optionally, the controller 202 may evaluate the level of influence agiven model parameter has on the control settings generated by thecontrol setting generator 218. The level of influence for a particularmodel parameter may represent an evaluation of how dependent the controlsettings are on variations within that particular model parameter.

Optionally, the controller 202 may evaluate the risk of a particularcontrol setting based on one or more model parameters used to determinethe control setting. The risk may be determined based on a combinationof the reliability and the level of influence of the given modelparameter.

The sensors 210 may be configured to measure the values of one or moreproperties of various components of the electromagnetic heating controlsystem 200. The sensors 210 may be configured to measure properties ofone or more of the signal generator 206, the load 208, and/or thehydrocarbon medium 209. Examples of the properties that may be measuredby the sensors 210 can include temperature, pressure, water desiccation,water diffusion, current, voltage, impedance, and frequency. The sensors210 can communicate with controller 202 to provide signals indicatingthe value/actual status of the measured property(ies).

The sensors 210 may include one or more sensors configured to measurespecific properties (e.g. temperature, pressure, current, etc.). Thesensors 210 may include a plurality of sensors positioned to measure thedifferent properties. In some cases, the sensors 210 may also include aplurality of sensors positioned to measure the same property, but atdifferent locations within the system (e.g. temperature sensorspositioned at different locations within the hydrocarbon medium 209).

Sensors 210 may be integrated with components of the system 200, such asload 208. For example, temperature sensors may be integrated with theload 208.

For example, the temperature sensors may include optical fiberspositioned within load 208. The optical fibers can be configured tomeasure temperatures along the load 208 using various techniques, suchas relying on the Raman scattering effect. The optical fibers may beused to detect temperature spikes or hot spots indicative of electricalarcing.

The load 208 may include an outer casing and the optical fibers may bepositioned inside the outer casing. Where the load 208 includes aplurality of radiators, the system 200 may include optical fiberspositioned within all of the radiators. Alternately, optical fibers maybe positioned within only a subset of the radiators. Alternate types oftemperature sensors may also be used that may provide increasedlongevity or reduced cost as compared to optical fibers.

The sensors 210 may include acoustic sensors. For example, acousticsensors may be positioned at the location of the coupling member 207.

Acoustic sensors may be used to determine the presence and/or locationof electrical arcing. Electrical arcing can cause rapid changes in thetemperature of the hydrocarbon medium 209, and these changes can causeacoustic vibrations in the load 208. The acoustic sensors can measurethe acoustic vibrations to detect the presence of the electrical arcing.

The sensors 210, such as acoustic sensors, may operate in conjunctionwith the signal generator 206 to determine the position of electricalarcing. For example, following the detection of an arc condition, thesignal generator 206 may be abruptly turned off (i.e. shut down) to stopthe electrical arcing and the resultant acoustic vibrations. There maybe a time delay between the shutoff of the signal generator 206 and theend of the acoustic vibrations detectable by the acoustic sensors(assuming the load 208 has sufficient length, typically greater than 10m). The length of the time delay can be used to determine theapproximate position of the electrical arcing.

In some cases, electrical arcing may occur at more than one locationalong the load 208. Deconvolution processing may then be used to isolateeach position. The deconvolution processing may involve calculationsbased on the geometry and acoustic properties of the load 208.

The sensors 210 may include probe sensors installed within thehydrocarbon medium 209. This may allow the system to evaluate the statusof properties of the hydrocarbon medium 209 at locations separated fromthe load 208 and/or signal generator 206. Alternately, probe sensors maybe omitted, e.g. due to installation costs concerns and/or concernsregarding sensor fragility.

The sensors 210 may include extracted sample sensors configured tomeasure the properties of samples of the extracted hydrocarbons. Thismay provide a more controlled environment within which to measureproperties of the hydrocarbons from the medium 209.

The sensors 210 can include current and/or voltage sensors positioned atone or more locations within the electromagnetic heating control system200. The current/voltage sensors may be configured with a high samplingrate (e.g. 50 MHz). This may enable the sensors to measure a widefrequency bandwidth.

The voltage/current sensors can be positioned at a plurality oflocations within the electromagnetic heating control system 200. Thevoltage and current measurements from the sensors in the system 200 canbe used to determine power dissipation between the different locationswithin system 200. The voltage and current measurements can also be usedto determine impedances within system 200.

The controller 202 may use various transforms (e.g. Fourier and inverseFourier transforms) to convert the measured values of the current and/orvoltage between time and frequency domains. This may allow thecontroller 202 to determine various time dependent or frequencydependent characteristics of the measured current and/or voltage.

The voltage and current measurements may be used to determine powerspectral densities within system 200. The determined power spectraldensities may be used to detect the presence of electrical arcing. Forexample, a pair of sensors (e.g. one current sensor, one voltage sensor)may be positioned at the signal generator 206 and another pair ofsensors may be positioned at load 208. The measured values determinedfrom the sensors at the signal generator 206 can be compared to themeasured values determined from the sensors at the load 208 to determinethe presence of electrical arcing.

Voltage and current sensors may be positioned at the output of thesignal generator 206. Voltage and current sensors positioned at theoutput of the signal generator 206 can measure the signals applied bythe signal generator 206 to the load 208. The controller 202 may use themeasurements at the signal generator output to determine variouscharacteristics of the load 208 (e.g. impedance) and/or the hydrocarbonmedium 209 (e.g. water concentration, temperature, and/or pressure). Thevoltage and current sensors may be operated before, during, and/or afterthe heating of the hydrocarbon medium 209.

The system 200 may be configured to operate the signal generator 206 toevaluate various properties of the coupling member 207, load 208, and/orthe hydrocarbon medium 209. The signal generator 206 can be configuredto emit sensing signals. The sensing signals can be transmitted alongthe coupling member 207 and/or load 208. The sensing signals may bereflected at various locations along the coupling member 207 and/or load208, and the reflected signals may return to the signal generator 206.Sensors positioned at the output of the signal generator 206 can be usedto measure the voltage and/or current of the emitted signals and thereflected signals.

The sensing signals may be reflected by changes in impedance along thecoupling member 207 or load 208. The reflected sensing signals cantravel back toward the signal generator 206 and the properties of thereflected signals can be measured by the voltage and current sensors.The controller may then use the properties of the emitted signals, andthe reflected signals, to determine various properties of the couplingmember 207, the load 208, and/or the hydrocarbon medium 209.

The signal generator 206 may be configured to emit a plurality ofsensing signals. The sensing signals may be emitted sequentially toallow changes in the system properties to be identified. The emittedsensing signals can be generated with a short signal duration (e.g.,several microseconds). These sensing signals may facilitate thedetection of rapidly changing properties. The sensing signals may beemitted on a continual (e.g. periodic) basis, to enable properties ofsystem 200 to be monitored.

In general, the sensing signals can be produced as generator waveletsoutput by signal generator 206. Various configurations of sensingsignals may be used. For example, the sensing signals may be emitted asone or more pulse signals. For example, a sequence of square waves maybe used as the sensing signals. This may help emphasize the observabledata related to various parameters of the load 208.

The signal generator 206 may emit a plurality of sensing signals toenable spatial resolution measurements to be performed. The plurality ofsensing signals may include a set of orthogonal pulse signals, whereeach of the pulse signals in the set of orthogonal pulse signals isorthogonal with respect to one another. For example, a set of sensingsignals generated using Walsh Hadamard functions (e.g. eight pulsesignals) can be used complete a measurement sweep across a largefrequency bandwidth.

FIGS. 5A-B and 6A-B illustrate various examples of how sensing signalsmay be applied in the system 200 to measure properties of system 200.

FIG. 5A shows a schematic illustration of an example measurement processin which the signal generator 206 generates and applies a sensing signal502 in the form of a pulse signal to the load 208 via the couplingmember 207. When the sensing signal 502 reaches the boundary between thecoupling member 207 and the load 208, a first portion 506 of the emittedsensing signal is reflected back toward the signal generator 206, whilea second portion 504 of the emitted sensing signal continues to travelalong the load 208. The reflected portion 506 can be measured by thevoltage and current sensors at signal generator 206. The controller maythen use the measurements of the reflected portion 506 to determinevarious properties of the transmitted portion 504, such as the impedanceof the load 208.

FIG. 5B shows a schematic illustration of another example measurementprocess in which the signal generator 206 generates and applies asensing signal 502 to the load 208 via the coupling member 207. In somecases, the hydrocarbon formation may include regions with differentlevels of water concentrations and corresponding impedances. Theseregions may also vary, or depend, on the degree or phase of heating.

As shown in FIG. 5B, the formation 209 includes a first region 550 and asecond region 552. In the example of FIG. 5B, the first region 550 has ahigh impedance (which may correspond to low water concentration) whilethe second region 552 has a low impedance (which may correspond to highwater concentration). The transmitted sensing signal 504 can bereflected at the boundary between the high impedance region 550 and thelow impedance region 552. This reflected portion 508 can propagate backtoward the signal generator 206 and be measured by the current andvoltage sensors. The measurements of the reflected portion 508 may beused to determine the location/extent of heating along the load 208.

FIG. 6A shows an example plot 600A of signals that may be emitted by thesignal generator 206 along with a plot 602A representing the resistanceof the hydrocarbon medium 209.

As shown in plot 600A, the signal generator 206 may emit an outputsignal 610A. The output signals 610A may be used to heat the hydrocarbonformations 209. The signal generator 206 may also emit sensing signals,in this case a plurality of pulse sensing signals 620A. As shown in FIG.6A, the signal generator 206 may emit the pulse sensing signals 620Aafter stopping transmission of the output signal 610A.

While the output signal 610A is being applied to the load 208, thehydrocarbon medium 209 is being heated. When the output signal 610A isno longer applied, since the hydrocarbon medium 209 is no longer beingheated the water concentration near the load 209 may increase, resultingin a decrease in resistance as shown in plot 602A. Reflected portions ofthe sensing signals 620A may be evaluated (e.g. voltage and currentmeasured by sensors at the signal generator 206) and used to determinethe change in the resistance over time. The change in resistance of timecan be used to determine various properties of the hydrocarbon medium209 e.g. properties related to the diffusion of water within hydrocarbonmedium 209, such as a diffusion time constant.

FIG. 6B shows an example plot 600B of signals that may be emitted by thesignal generator 206 along with a plot 602B representing the resistanceof the hydrocarbon medium 209.

As shown in plot 600B, the signal generator 206 may emit sensing signals620B interspersed amongst output signals 610B intended for load heating.While the output signals are applied to the load 208, the hydrocarbonmedium 209 is heated and the water concentration near the load 209decreases, resulting in an increase in resistance. Reflected portions ofthe sensing signals 620B may be evaluated (e.g. voltage and currentmeasured by sensors at the signal generator 206) and used to determinethe change in the resistance over time. The change in resistance of timecan be used to determine various properties of the hydrocarbon medium209 during heating, e.g. properties related to the diffusion of waterwithin hydrocarbon medium 209, such as a diffusion time constant.

Referring again to FIG. 2A, the data sources 212 may provide varioustypes of data to the controller 202. The data can include informationrelated to the load 208, the signal generator 206, and/or thehydrocarbon medium 209. For example, the data may include dielectricproperties, chemical composition, water composition, etc. of thehydrocarbon medium 209. The data sources 212 may include measurements ofdrilling core samples from installation of the load 208, data related toother hydrocarbon mediums similar in structure or composition to thehydrocarbon medium 209, or general hydrocarbon reservoir data.

The data sources 212 may also include production configuration data. Forexample, the production configuration data may include a preferredheating or hydrocarbon production strategy.

The electromagnetic heating control system 200 may also include othercomponents that are not shown in FIG. 2A. Such other components may becontrolled by controller 202 via control setting generator 218 and maybe included in the system model defined by the predictive model 204.

For example, the electromagnetic heating control system 200 may includea solvent control system (not shown). The solvent control system cancontrol the pumping or injection of a solvent, such as water, into thehydrocarbon medium 209. Solvent may be injected to increase heattransfer from the load 208 to the hydrocarbon medium 209 and/or increasethe flow of hydrocarbons within the hydrocarbon medium 209.

Referring now to FIG. 4 , shown therein is an example method 400 ofoperating the electromagnetic heating control system 200. Method 400 maybe implemented using systems for electromagnetic heating of ahydrocarbon medium such as systems 100 and 200 described herein above.

At 410, the controller 202 can determine a current operational stateusing a model of at least the hydrocarbon medium and the load. Forexample, the controller 202 may use the predictive model 204 todetermine the current operational state.

As discussed herein above, the predictive model 204 can model variouscomponents of the electromagnetic heating control system 200, such asthe signal generator 206, the load 208, and the hydrocarbon medium 209.

The current operational state can include various aspects of theelectromagnetic heating control system 200 modeled by the predictivemodel 204. The current operational state can include information relatedto the present status or condition of properties of the electromagneticheating control system 200.

For example, the current operational state may include propertiesrelated to the hydrocarbon medium 209 such as a temperature profile, awater concentration profile, a hydrocarbon concentration profile, apressure profile, an electromagnetic profile, etc. Alternately or inaddition, the current operational state may include properties relatedto the load 208 such as a standing electromagnetic wave profile, atemperature profile, etc. Alternately or in addition, the currentoperational state may include properties related to the signal generator206 such as an output signal profile, a temperature profile, etc.

The controller 202 may update parameters used by the predictive model204 in order to determine the current operational state. The controller202 can update the predictive model 204 by updating the status of one ormore of the model parameters.

For example, the status of one or more model parameters may bedetermined using measured properties of the electromagnetic heatingcontrol system 200. For example, sensors 210 can be used to determinethe actual status of various properties of the signal generator 206, theload 208, and/or the hydrocarbon medium 209, such as temperature,pressure, water concentration, current, voltage, impedance, andfrequency, etc. The controller 202 can receive the measurements from thesensors 210 and update the status of the model parameters to reflect theactual status of those parameters.

In some cases, the sensors 210 may not measure the status of theparameters directly. The status of one or more model parameters may bedetermined based on at least one observable of the system state. Theobservables may be used to determine the actual status of one or moreproperties directly. Alternately, the observables may be used to inferthe actual status of one or more properties.

Optionally, the controller 202 may compare the measured properties withpredicted properties from the predictive model 204, e.g. using theprocess illustrated in FIG. 2D. The controller 202 may determine whetherthe status of the model parameters needs to be updated based on thecomparison. If an update of the model parameter is required, thecontroller 202 can use the measured status to update the model parameterto reflect the actual status as measured by the sensors 210.

In some embodiments, the controller 202 can determine the status of oneor more model parameters based on a machine learning model. For example,an artificial neural network may be trained to generate a predictedstatus of one or more model parameters based on inputs supplied by thecontroller 202. In some embodiments, the controller 202 can determine apredicted status of one or more model parameters based on historicaldata. For example, the controller 202 can determine the predicted statusof one or more model parameters based on historical data received fromthe data sources 212.

At 420, the controller 202 determines a desired operational state basedon the current operational state and a desired heating life cycle. Thedesired heating life cycle can define a heating profile for the load209. The heating profile defined by the desired heating life cycle mayvary with time, e.g. based on the stage of heating of medium 209. Thedesired heating life cycle may be defined, for example, by a life cyclesub-unit 230 as described herein above. The desired heating life cyclecan include information related to a status or condition of theelectromagnetic heating control system 200.

Similarly, the desired operational state can include information relatedto a status or condition of the electromagnetic heating control system200. However, in contrast to the current operational state, the desiredoperational state can define a desired status or condition that thecontroller 202 wishes to achieve at a future time. For example, thedesired operational state may include at least one of a specifiedspatial heating profile along a length of the load, a specified powerspectral density of the output signal, and a specified standingelectromagnetic wave pattern along a length of the load.

The desired operational state may be determined based on the desiredheating life cycle for the medium 209. The desired operational state canbe selected for a future time in order to maximize the fit between thedesired operational state and a desired state of the desired heatinglife cycle at the future time. That is, the desired status or conditiondefined by the desired operational state may be selected to provide amatch, or near match, to the status or condition defined by the desiredheating life cycle for the future time.

The desired operational state may be modeled by the predictive model204. For example, each desired operational state may include aparticular spatial heating profile along a length of the load 208, aparticular standing electromagnetic wave pattern along a length of theload 208, and/or a particular power spectral density of the outputsignal generated by signal generator 206. These state characteristicsdefined by each potential operational state can be compared to thecorresponding characteristics defined by the desired heating life cyclefor the same future time in order to identify the desired operationalstate.

An example characteristic of a state characteristic defined by thedesired heating life cycle may include a uniform heating profile. Auniform heating profile may be desirable to encourage level hydrocarbonproduction across the hydrocarbon medium 209.

Another example characteristic of a state characteristic defined by thedesired heating life cycle may include a targeted heating profile. Atargeted heating profile may focus heat to regions that have a highconcentration of hydrocarbons and minimize heating in areas that have alow concentration of hydrocarbons. This may promote more efficientheating, by reducing the energy consumption in regions having a lowconcentration of hydrocarbons. The targeted heating profile defined bythe desired heating life cycle may vary depending on the stage of theheating life cycle of the medium 209.

Another example characteristic of a state characteristic defined by thedesired heating life cycle may include maintaining a particular waterconcentration. The particular water concentration defined by the desiredheating life cycle may vary depending on the stage of the heating lifecycle of the medium 209.

Another example characteristic of a state characteristic defined by thedesired heating life cycle may include minimizing the likelihood ofelectrical arcing. For example, the desired heating life cycle mayrequire a standing wave pattern that does not include regions ofexcessive voltage. This may help minimize electrical arcing and thushelp reduce the risk of damage to equipment.

Another example characteristic of a state characteristic defined by thedesired heating life cycle may include a desired arcing condition. Insome cases, it may be desirable to cause electrical arcing in order togenerate high frequency (relative to the frequency of the output signal)electromagnetic waves. The high frequency electromagnetic waves maytravel further distances than the standing electromagnetic waves andaccordingly heat regions of the hydrocarbon medium 209 that are locatedfurther from the load 208. The electrical arcing can cause hightemperatures resulting in pyrolysis of the hydrocarbons within thehydrocarbon medium 209. The processed hydrocarbons may have smallerchains that can more easily be transported. The electrical arcing canalso generate hydrogen by breaking down water, which can aid inpyrolysis.

The controller 202 can determine the desired operational state byevaluating the expected operational cost of one or more potentialoperational states, for example as described above in relation to FIG.2E. The desired operational state may be selected from the possibleoperational states in order to minimize the expected operational cost.

For example, the controller 202 may attempt to minimize an operationalcost function. An operational cost function can include a plurality ofcosts associated with a plurality of potential operational states. Eachcost can correspond to a penalty or loss associated with a particularpotential operational state. Generally, a higher cost can correspond toa less desirable operational state, whereas a lower cost can correspondto a more desirable operation state.

The expected operational cost of a potential operational state may bedetermined based on cost factors such as the energy loss during heatingof the hydrocarbon medium. Energy loss during heating of the hydrocarbonmedium may be determined as the difference between input energy suppliedto the signal generator and heat energy supplied to the hydrocarbonmedium.

The expected operational cost of the potential operational state mayalso be constrained by at least one operational constraint for thesignal generator. For example, the signal generator may have one or moreoperational constraints such as a voltage range, a current range, afrequency range, a temperature range, a maximum heating and productioncompletion time, and a minimum power, possible generator switch statesand so forth.

For example, the controller 202 may determine a plurality of potentialoperational states using the predictive model 204. Each potentialoperational state can be modeled by the predictive model 204 usingdifferent values for the modeling parameters. Each potential operationalstate can correspond to a different status or condition of theelectromagnetic heating control system 200. The controller 202 may thenevaluate the expected operational costs associated with each potentialoperational state, and assign the determined cost to each potentialoperational state. The controller 202 can then select the potentialoperational state associated with the lowest total cost as the desiredoperational state. The controller 202 may be configured to limit thepotential operational states to only those in which the generator 206 iscapable of operating (e.g. based on the generator operationalconstraints).

The various potential operational states may correspond to differentheating profiles, standing electromagnetic waves, or output signals. Thepotential operational states may correspond to different configurationsof the various components of the electromagnetic heating control system200. For example, each potential operational state may correspond to adifferent configuration of the signal generator 206 (a different set ofsignal generator control settings). Multiple configurations of thesignal generator 206 (i.e. set of signal generator control settings) mayresult in the same or very similar output signal or output impedance.However, different signal generator control settings may requiredifferent energy inputs or result in different energy losses, thusaffecting the expected operational cost.

The expected operational cost may incorporate different types of costsfor the potential operational states. For example, the cost function mayinclude costs related to energy loss during heating of the hydrocarbonmedium 209 (i.e., the difference between input energy supplied to thesignal generator 206 and heat energy supplied to the hydrocarbon medium209). The cost function may also include costs corresponding to theefficacy of hydrocarbon extraction (i.e., the residual amount ofhydrocarbon that would remain in the hydrocarbon medium 209).

The controller 202 can also minimize the cost function in accordancewith one or more constraints. Accordingly, the minimization of the costfunction may be referred to as a constrained optimization.

The constraints may include hard constraints and/or soft constraints. Ahard constraint may limit the potential operating states evaluated bythe controller 202. That is, the controller 202 may not select apotential operational state that fails to satisfy a hard constraint(such as a generator operational constraint). A soft constraint may addan additional cost or penalty to particular operation state.

The constraints may be related to operating ranges of the components ofthe electromagnetic heating control system 200. For example, for thesignal generator 206, the constraints may include a voltage range, acurrent range, a frequency range, and/or a temperature range over whichthe signal generator 206 is operational (or is effectively operational).The constraints may be related to the maximum capability of a componentor a maximum safety rating. For example, the constraints may be selectedto prevent electrical arcing and/or damage to equipment.

Alternately or in addition, the constraints may be related to theefficiency of the electromagnetic heating control system 200. Forexample, the constraints may include a maximum completion time forhydrocarbon extraction, a minimum power, a maximum energy expenditure,or a maximum financial cost.

Alternately or in addition, the constraints may be related to a requiredheating profile. The heating profile may limit heating in specificregions to prevent overheating and equipment damage. The heating profilemay be required to ensure efficient hydrocarbon production.

The controller 202 can minimize the cost function using variousdifferent types of evaluation algorithms and methods. There may be alarge possible number of potential operating states—for example, theremay be upwards of 2¹¹⁶ possible potential operating states in somecases. Accordingly, some of the possible potential operating states maybe eliminated using a rules based algorithm. The controller 202 may usea genetic algorithm to evaluate the expected operational cost of thepotential operating states.

It should be noted that the minimization of a cost function describedherein need not refer to a global minimum. For example, where there area large number of possible potential operational states, minimizing thecost function may refer to a local minimum within a selected range ofpotential operational states.

At 430, the controller 202 determines one or more desired controlsettings for the signal generator 206 to achieve the desired operationalstate. For example, the desired control settings for the signalgenerator 206 may include a voltage setting, a current setting, afrequency setting, a sequence of state transitions etc.

The controller 202 can determine the desired control settings in avariety of ways. In some embodiments, the controller 202 can determinethe desired control settings based on a machine learning model. Forexample, an artificial neural network may be trained to determine thedesired control settings based on inputs supplied by the controller 202,such as predictions from the predictive model 204 of the desiredoperating state. Alternately or in addition, the controller 202 maydetermine the desired control settings using historical configurationdata. For example, the controller 202 can determine the desired controlsettings based on historical data received from the data sources 212.FIG. 2E illustrates an example iterative process for determining desiredcontrol settings.

For example, for a desired spatial heating profile, the controller 202may determine a particular standing wave pattern that can achieve adesired spatial heating profile. The controller 202 may then determine aparticular power spectral density for an output signal that, whenapplied to the load 208, can generate the particular standing wavepattern. The controller 202 can then determine control settings for thesignal generator 206 to generate an output signal having the particularpower spectral density.

The controller 202 may also determine one or more desired controlsettings for other components of the electromagnetic heating controlsystem 200 to achieve the desired operational state. For example, thecontroller 202 may determine one or more desired control settings forthe load 208. In another example, the controller 202 may determine oneor more desired control setting for a solvent control system (notshown). The solvent control unit may be configured to provide a solventto the hydrocarbon medium 209.

At 440, the controller 202 can apply the one or more desired controlsettings to the signal generator 206. The signal generator 206 can thengenerate an output signal. The output signal can then excite the load208, thereby heating the hydrocarbon medium 209. This may facilitateextraction of hydrocarbons from the hydrocarbon medium.

Alternatively or in addition, controller 202 may also apply any desiredload control setting(s) to the load 208. Alternatively or in addition,controller 202 may also apply any desired solvent control setting to asolvent control unit.

In some embodiments, the desired control settings can cause the signalgenerator 206 to generate a pulsed output signal. For example, referenceis now made to FIG. 7 , which illustrates an example plot of a pulsedoutput signal. As shown in FIG. 7 , the signal generator 206 can berepeatedly turned on for a dwell time of T1 and turned off for dwelltime of T2.

As shown in the example of FIG. 7 , the resultant output signal can havea first dwell state 710 having a non-zero amplitude during T1 and asecond dwell state 720 having a zero amplitude during T2. During theactive dwell time T1, water may diffuse away from the load 208 as theregion of the hydrocarbon medium 209 around the load 208 is heated.Accordingly, the resistance of the region can increase during T1. Duringthe inactive dwell time T2, water may diffuse back toward the load 208,increasing the resistance of the region of the hydrocarbon medium 209.

As illustrated in the example of FIG. 7 , pulsing the output signal mayallow the resistance of the hydrocarbon medium 209 to be maintainedwithin a specific range. The pulsed output signals may thus be used tocontrol the impedance of the load 208.

The length of each of T1 and T2 may be determined based on diffusionproperties of the hydrocarbon medium 209. The diffusion properties ofthe hydrocarbon medium 209 may, in turn, be determined using sensors210, for example using the methods described with respect to FIGS. 6A-Band/or using predictive model 204.

Referring again to FIG. 4 , the controller 202 may, in some embodiments,apply one or more desired control settings to other components of theelectromagnetic heating control system 200. For example, the controllermay apply desired control settings to the load 208 or the solventcontrol system (not shown).

Optionally, the method 400 can be repeated or looped as shown in FIG. 4. That is, following the completion of step 440, method 400 may repeatagain beginning back at step 410. The electromagnetic heating controlsystem 200 may repeat the process illustrated in FIG. 4 to operate in alive or continuous manner.

For example, the electromagnetic heating control system 200 mayreconfigure various aspects of the system 200 in response to changingconditions in the hydrocarbon medium 209 (e.g. as shown in FIG. 2D). Thepredictive model 204 can be updated to reflect the actual status and/orupdated predicted status of the model parameters and new controlsettings can be generated.

The electromagnetic heating control system 200 may also be used in somecases where the system 200 does not heat the hydrocarbon medium 209directly. For example, the electromagnetic heating control system 200may be implemented with a SAGD system. In the SAGD system, injectedsteam is used to heat the hydrocarbon medium 209 instead of theelectromagnetic waves. In such implementations, the load 208 may not beused to heat the hydrocarbon medium 209 directly. Rather, the load 208may be used to generate probe signals to measure various properties ofthe steam injection.

As used herein, reference to the load may be understood to include theelectrical load of the radiating structure (e.g. conductors 112,radiating structures 208 etc.) immersed within the hydrocarbon mediumand any electrical connection apparatus (e.g. waveguide portion 110,coupling member 207) to the generator (e.g. generators 108/206).

Numerous specific details are set forth herein in order to provide athorough understanding of the exemplary embodiments described herein.However, it will be understood by those of ordinary skill in the artthat these embodiments may be practiced without these specific details.In other instances, well-known methods, procedures and components havenot been described in detail so as not to obscure the description of theembodiments. Furthermore, this description is not to be considered aslimiting the scope of these embodiments in any way, but rather as merelydescribing the implementation of these various embodiments.

1.-21. (canceled)
 22. A system for controlling electromagnetic heating ahydrocarbon medium using a signal generator and a load having afrequency dependent and time dependent and amplitude dependentimpedance, the system comprising: a processor configured to: determine adesired heating life cycle for the hydrocarbon medium; determine acurrent operational state, using a model of at least the hydrocarbonmedium and the load; determine a desired operational state based on thecurrent operational state and the desired heating life cycle, whereinthe desired operational state is selected to maximize a fit between thedesired operational state and the desired heating life cycle; determineat least one desired signal generator control setting for the signalgenerator, wherein the at least one desired signal generator controlsetting is selected to provide the desired operational state; and applythe at least one desired signal generator control setting to the signalgenerator, wherein the signal generator generates an output signal inresponse to the applied at least one desired signal generator controlsetting, and wherein the output signal is defined to excite the load andthereby heat the hydrocarbon medium.
 23. The system of claim 22, whereinthe processor is configured to: determine the desired heating life cycleto include a heating profile for the load, wherein the heating profilevaries with time; determine the current operational state for a presenttime; and select the desired operational state for a future time tomaximize the fit between the desired operational state and a desiredstate of the desired heating life cycle at the future time.
 24. Thesystem of claim 22, wherein the processor is configured to: determinethe current operational state for a present time; determine a differencebetween the current operational state for the present time and thedesired heating life cycle for the present time; and update the desiredheating life cycle using the difference.
 25. The system of claim 22,wherein the load comprises at least one radiating structure positionedin the hydrocarbon medium, and when the load is excited by the outputsignal, electromagnetic energy is coupled into the hydrocarbon medium bythe load.
 26. The system of claim 22, wherein: the at least one desiredsignal generator control setting defines a sequence of statetransitions; the processor is configured to apply the at least onedesired signal generator control setting to the signal generator byadjusting the signal generator between a plurality of signal generatorstates according to the sequence of state transitions; and the sequenceof state transitions are defined to provide a desired waveform for theoutput signal.
 27. The system of claim 22, wherein the model comprisesat least one model parameter and the processor is configured todetermine the current operational state by: determining a status of theat least one model parameter; generating an updated model by updatingthe model using the status of the at least one model parameter; anddetermining the current operational state from the updated model. 28.The system of claim 27, wherein each model parameter in the at least onemodel parameter comprises an expected status of one or more propertiesof at least one of the signal generator, the load, and the hydrocarbonmedium, wherein the one or more properties comprises at least one oftemperature, pressure, water concentration, current, voltage, impedance,and frequency; and the system further comprises: at least one sensoroperable to measure an actual status of the one or more properties of atleast one of the signal generator, the load, and the hydrocarbon medium;and wherein determining the status of the at least one model parametercomprises: for a given model parameter in the at least one modelparameter: determining, using the at least one sensor, the actual statusof the one or more properties of at least one of the signal generator,the load, and the hydrocarbon medium corresponding to that given modelparameter; and updating the expected status to correspond to the actualstatus.
 29. The system of claim 28, wherein determining the actualstatus of the one or more properties comprises: applying at least onesensing signal to the load; measuring at least one reflected sensingsignal from the load; and determining the actual status of the one ormore properties using the at least one reflected sensing signal.
 30. Thesystem of claim 29, wherein determining the actual status of the one ormore properties comprises: prior to applying the at least one sensingsignal to the load, applying an output signal from the signal generatorto the load.
 31. The system of claim 29, wherein determining the actualstatus of the one or more properties comprises: prior to applying the atleast one sensing signal to the load, disabling an output signal fromthe signal generator to the load.
 32. The system of claim 29, whereinthe at least one sensing signal comprises at least two sensing signals,each of the at least two sensing signals being orthogonal with respectto the other sensing signals.
 33. The system of claim 27, wherein theprocessor is configured to determine the status of the at least onemodel parameter based on at least one of historical data and a machinelearning model.
 34. The system of claim 22, wherein the model comprisesat least one of an electromagnetic property, a thermal property, a fluidproperty, and a structural property.
 35. The system of claim 22, whereinthe model comprises a transverse electromagnetic mode forming a standingwave along a length of the load.
 36. The system of claim 22, wherein thedesired operational state is determined based on at least one constraintfor the signal generator, and the at least one constraint for the signalgenerator comprises at least one of a voltage range, a current range, afrequency range, a temperature range, a maximum completion time, aminimum power, and a maximum power.
 37. The system of claim 22, whereinthe desired operational state comprises at least one of a spatialheating profile along a length of the load, a power spectral density ofthe output signal, and a standing electromagnetic wave pattern along alength of the load.
 38. The system of claim 23, wherein the processor isconfigured to determine the desired operational state by: determining aplurality of potential operational states based on the model;determining a plurality of potential cost penalties by, for eachpotential operational state in the plurality of potential operationalstates determining a potential cost penalty associated that potentialoperational state using the desired heating life cycle; determining aminimum cost operational state of the plurality of potential operationalstates, the minimum cost operational state associated with a lowest costpenalty of the plurality of cost penalties; and identifying the minimumcost operational state as the desired operational state.
 39. The systemof claim 22, wherein the desired operational state comprises at leastone arcing condition.
 40. The system of claim 22, wherein the processoris further configured to determine the at least one desired signalgenerator control setting based on at least one of historical data and amachine learning model.
 41. The system of claim 22, wherein theprocessor is further configured to: determine at least one desired loadcontrol setting for the load based on the desired operational state; andapply the at least one desired load control setting to the load.
 42. Thesystem of claim 22, wherein the processor is further configured to:determine at least one desired solvent control setting for a solventcontrol unit based on the desired operational state, the solvent controlunit for providing solvent to the hydrocarbon medium; and apply the atleast one desired solvent control setting to the solvent control unit.43. (canceled)